Preamble

From Resource Repository to Smart Services: Research on the Construction Model and Empowerment Path of a Panoramic Data System for Chinese Excellent Traditional Culture in University Libraries
Hunan Nonferrous Metals Vocational and Technical College, Zhuzhou, 412000

Abstract

This study investigates how university libraries can transcend their conventional role as "resource repositories" and transform into providers of "smart services" by constructing a panoramic data system for Chinese excellent traditional culture. The paper first analyzes current challenges in traditional culture resource development within university libraries, including data silos and monolithic service models. It then proposes the core concept of a "panoramic data system"—an integrated framework that merges multimodal resources, links knowledge ontologies, and supports panoramic research and experiential engagement. The study focuses on the system's "construction model" (encompassing coordinated development across resource, data, platform, and service layers) and "empowerment paths" (how to deliver smart enablement for teaching, research, cultural inheritance, and public engagement). Finally, through case analysis and future outlook, it provides theoretical foundations and practical guidance for the digital transformation and innovative service development of university libraries.

Keywords: University Libraries; Chinese Excellent Traditional Culture; Panoramic Data System; Knowledge Services; Smart Empowerment; Digital Humanities
Classification Number: G250
Research Methods: Literature review method (synthesizing domestic and international theories and practices); Case analysis method (selecting representative domestic and international cases); Model construction method (proposing construction and empowerment path models for the "panoramic data system").

Introduction

Culture constitutes the spiritual lifeline and creative source of a nation. Chinese excellent traditional culture embodies the deepest spiritual pursuits of the Chinese people and serves as the foundation for maintaining our cultural footing amidst global exchanges. Promoting the creative transformation and innovative development of Chinese culture has become a major national strategic priority. Concurrently, digital technologies—represented by big data, artificial intelligence, and virtual reality—are profoundly reshaping how cultural resources are preserved, studied, disseminated, and experienced, offering unprecedented opportunities to "rediscover" and "activate" traditional cultural treasures.

In this context, university libraries, as crucial hubs for cultural inheritance and knowledge innovation, face profound reconstruction of their roles and functions. While they have long excelled as indispensable "resource repositories" for collecting and preserving Chinese traditional cultural materials, conventional resource development models exhibit significant limitations: digital collections often exist as isolated "information silos" with homogeneous resource types and low knowledge connectivity; service models remain relatively passive, struggling to meet the urgent demands of digital-native users for immersive, interactive, and knowledge-intensive services. Consequently, vast quantities of precious collections fail to unleash their full knowledge value and cultural appeal, and the library's smart empowerment potential remains unrealized. Breaking free from the "emphasis on collection over utilization" paradigm and transitioning from static "resource repositories" to dynamic "smart services" represents the core challenge in contemporary university library transformation.

The concept of "panorama" offers new perspectives for this transformation. It suggests that the digital reconstruction of Chinese excellent traditional culture should not stop at fragmented, isolated digital copies, but rather strive to build an organic entity that integrates multimodal resources—including text, images, audio-video, and 3D models—while revealing their intrinsic knowledge associations. Constructing such a "panoramic data system for Chinese excellent traditional culture" aims to achieve comprehensive digitization of cultural resources, full-dimensional correlation, and full-scenario servitization, thereby providing a powerful data foundation and smart engine for academic research, education, and cultural dissemination.

Current domestic scholarship on library traditional culture resource digitization is extensive but largely concentrates on specific technical applications or single-database construction, lacking systematic exploration of the entire chain from "system construction" to "service empowerment." This study therefore addresses the core questions: How should university libraries construct a panoramic data system for Chinese excellent traditional culture? And how can this system empower teaching, research, and cultural dissemination to realize the path transformation from "resource repository" to "smart services"?

To answer these questions, this paper first analyzes the internal logic of the role evolution from "resource repository" to "smart service" in university libraries. It then systematically elaborates the core connotation of the "panoramic data system" and proposes a "four-layer coordinated" construction model comprising resource, data, platform, and service layers. Building upon this foundation, the study explores diverse empowerment paths for research innovation, teaching, cultural dissemination, and public engagement. Finally, it analyzes potential challenges in practice and proposes corresponding countermeasures. This research aims to provide a useful reference framework and practical guidance for university libraries to deepen service connotations and enhance cultural inheritance innovation capabilities in the digital age.

1.1 Research Background and Significance

Policy Background: Responding to the national "Cultural Digitalization Strategy" and "Chinese Excellent Traditional Culture Inheritance and Development Project."
Technical Background: Big data, artificial intelligence, and virtual reality technologies enable deep digital development of cultural heritage.
Industry Background: University libraries face intrinsic needs to transform from "knowledge treasure houses" to "smart brains," requiring enhanced service capabilities and influence.
Theoretical Significance: Enriching theoretical frameworks in library science and digital humanities, exploring new paradigms for resource construction and knowledge services.
Practical Significance: Providing actionable solutions for university libraries to construct traditional culture data systems, enhancing their cultural inheritance and innovation functions.

1.2 Literature Review

1.2.1 International Research Status

European and American university libraries have developed mature theoretical and practical systems in special collection digitization, digital humanities project support, and open data services. Their core characteristic is the transformation from "resource custodians" to "academic partners" and "data service providers."

In special collection digitization, Western university libraries universally view it as a core mission aimed at permanent preservation and global sharing of cultural heritage. Their digitization practices extend beyond simple image scanning to emphasize metadata standardization (widely adopting MODS, METS, EAD standards), meticulous copyright handling, and sustainable digital preservation strategies. For example, Harvard Library's "Digital Collections" project provides massive high-quality digitized special collections, with comprehensive metadata ensuring resource discoverability and interoperability [1].

In digital humanities (DH) project support, university libraries have transcended technical assistance to become key enablers of interdisciplinary research. Their service models primarily include: establishing specialized service centers (e.g., Stanford University Library's "Center for Interdisciplinary Digital Research (CIDR)" provides advanced research method consulting and technical training in GIS spatial analysis, text mining, and data visualization [2]); developing dedicated tools and platforms (e.g., Oxford University Library utilizes the IIIF (International Image Interoperability Framework) protocol, enabling global scholars to compare, annotate, and reuse image resources within a unified framework, greatly promoting academic collaboration [3]); and providing full project lifecycle management, where librarians as collaborators deeply participate in DH project conception, data acquisition, cleaning, modeling, analysis, and publication, embodying an "embedded" service philosophy [4].

In open data services, European and American university libraries actively practice open science principles, leading or participating in institutional repository and research data management (RDM) services. They not only provide guidance on data management plan (DMP) writing, data storage, and publication services for faculty and students, but also strive to release special collection digital resources as open data, allowing users to freely download, reuse, and conduct computational analysis, thereby maximizing academic value [5].

Overall, international research and practice exhibit characteristics of deep technology integration, emphasis on open sharing, and focus on service embedding, with the ultimate goal of activating special collection resources to empower cutting-edge humanities and social science research.

1.2.2 Domestic Research Status

Domestic research on university libraries and Chinese excellent traditional culture is abundant, primarily concentrating on resource construction, technology application, and service transformation, though with a tendency toward "emphasizing construction over application."

In traditional culture resource construction, numerous studies focus on building characteristic databases. Many university libraries have established excellent projects such as "Tsinghua University Architecture Digital Library" and "Peking University Miji Linlang" based on ancient books, local documents, and intangible cultural heritage resources. These studies explore key aspects like resource selection, metadata standardization, and digitization workflows [6]. However, many databases suffer from "information silo" phenomena with inconsistent standards and low interoperability.

In smart library services, domestic scholars have begun exploring applications of AI, big data, and VR/AR in library services, including smart space reconstruction, intelligent consultation robots, and personalized recommendation services [7]. However, successful cases and systematic research on deeply integrating these technologies with traditional culture resources to create immersive, experiential cultural services remain relatively scarce.

Current research limitations include: first, a focus on the "construction" phase—how to digitize and build databases—with insufficient exploration of effective service models and empowerment paths for actually "using" resources and embedding them into teaching, research, and public cultural life. Second, existing practices are fragmented and point-based, lacking a "panoramic" perspective that integrates resources, technology, services, and scenarios. Various systems, platforms, and data fail to form organic systems, making it difficult to support macro, multidimensional, and deep cultural insights and knowledge discovery.

1.2.3 Research Review

In summary, domestic and international research provides a solid theoretical and practical foundation for this study. The deeply embedded model of digital humanities and open data services in foreign university libraries offers important references for positioning libraries as "academic empowerment centers." Domestic research has accumulated rich experience in the ontological construction of traditional culture resources.

However, existing research gaps also create innovation space for this study. Neither domestic nor international practice has systematically proposed the concept of a "panoramic data system for Chinese excellent traditional culture" and its construction model. This study aims to bridge the gap between "construction" and "utilization" with two key innovations: first, a systematic perspective that breaks the isolated construction model of traditional characteristic databases, emphasizing the building of a "panoramic data" system that integrates heterogeneous data (text, images, audio, video, spatiotemporal information), follows unified standards, and enables cross-database retrieval and interoperability. Second, a focus on empowerment paths that not only addresses "how to construct" but more importantly "how to empower," systematically exploring how the panoramic data system can activate traditional culture resource value through smart service paths (e.g., data-driven research support, contextual cultural experiences, open innovative educational empowerment) to ultimately achieve the paradigm shift from "resource repository" to "smart services."

1.3 In-Depth Case Analysis: From Practical Exploration to Model Insights

To anchor theoretical concepts in practical foundations, this study selects two benchmark cases—one domestic and one international—for deep deconstruction. They respectively represent "platform-driven" and "data-core" construction paradigms, jointly outlining the diverse landscape of panoramic data system construction.

1.3.1 Domestic Case: Peking University Digital Humanities Open Laboratory—Platform-Driven Integration and Empowerment

The practice of Peking University Digital Humanities Open Laboratory demonstrates the ambition of university libraries transforming from resource custodians to research infrastructure providers. Its core characteristic lies in building a unified digital scholarship platform that integrates heterogeneous resources and directly provides tool-based services for complex research needs in humanities and social sciences.

1.3.1.1 Construction Model Analysis

Resource Layer: Its foundation is aggregative rather than single-sourced. It systematically digitizes various documents from Peking University Library's collections—including ancient books, maps, rubbings, and Republican-era newspapers—as well as resources obtained through partnerships with external institutions, forming a multimodal resource pool across types and carriers.

Data Layer: This is the key to its "smart" leap. The laboratory goes beyond basic OCR text conversion to focus on deep semantic annotation and data structuring. For example, in the "Complete Tang Poems Analysis Platform," it not only provides poetic texts but also extracts and associates entities such as poets, dynasties, place names, and official positions, preliminarily constructing a knowledge graph for the Tang poetry domain and achieving the transformation from "text database" to "knowledge database" [8].

Platform Layer: The laboratory has built an online open-source platform integrating storage, computation, visualization, and analysis. This platform provides a series of digital humanities tools—including social network analysis, geographic information systems (GIS), text mining, and visualization—enabling researchers to analyze platform data online without installing complex software locally, thus realizing a paradigm shift from "data follows people" to "tools follow data" [9].

Service Layer: Its empowerment path directly targets the core of research and teaching. On one hand, it provides data and technical support for high-level research projects (e.g., National Social Science Fund projects). On the other hand, through "course-embedded services," it integrates with courses like "Chinese Historical Geography" and "Ancient Chinese Literature," enabling students to complete data analysis assignments using the platform and transforming dormant resources into vivid teaching materials that greatly stimulate student interest and research capabilities.

1.3.1.2 Insights and Limitations

The PKU Laboratory model proves that university libraries can become catalysts for academic innovation by building centralized platforms that lower the technical threshold for digital humanities. Its success hinges on the breadth of resource integration, depth of data processing, and precision of service embedding. However, this model demands extremely high professional technical teams from libraries themselves and faces long-term challenges in sustainable operation and maintenance.

1.3.2 International Case: Harvard University China Biographical Database (CBDB)—Data-Centric Openness and Co-creation

Unlike PKU's comprehensive platform, Harvard's CBDB project follows a "narrow but deep" path. Rather than pursuing full coverage of resource types, it focuses on the core entity of "Chinese historical figures" through extreme data standardization and open sharing to build a global academic infrastructure.

1.3.2.1 Construction Model Analysis

Resource Layer: Highly focused, its resources primarily consist of biographical information recorded in dynastic histories, epitaphs, and biographical literature. It is essentially a thematic, relational database.

Data Layer: CBDB has perfected its construction model. It establishes extremely rigorous and detailed data models with standardized definitions and coding for figures' birth and death years, native places, relatives, official careers, and social relationships. This highly structured and normalized data processing enables seemingly unrelated biographical information from different sources to be interconnected and compared, forming a massive figure-relationship knowledge graph [10].

Platform Layer: CBDB itself does not provide complex online analysis platforms; its "platform" focuses more on data distribution and API services. It allows global users to freely download the complete database (in multiple formats) or access data through API interfaces. This extreme openness completely transfers data usage rights and creative power to the academic community [11].

Service Layer: Its empowerment path "teaches people to fish." By providing pure, reliable structured data, CBDB has empowered thousands of research projects worldwide. Scholars use this data with their familiar statistical analysis software (e.g., Python, R), social network analysis tools (e.g., Gephi), or GIS software to conduct in-depth studies on major historical issues such as the imperial examination system, elite networks, and regional mobility. Its service is intangible but its impact is ubiquitous, truly achieving data "interoperability" and "reusability."

1.3.2.2 Insights and Limitations

CBDB's success demonstrates the construction philosophy that "data quality trumps functional complexity." It proves that a standardized, fully open database focused on a core entity generates far greater academic energy than a flashy but loosely-structured system. It provides a model for how university libraries with limited resources can achieve tremendous impact through "single-point breakthroughs." Its limitations lie in indirect service delivery, which is less friendly to users unfamiliar with data analysis tools, and its construction highly depends on long-term, professional academic community collaboration.

1.3.3 Comprehensive Analysis Conclusions

Through comparative analysis of the PKU Laboratory and Harvard CBDB, we derive the following core conclusions that provide solid support for this study's construction model and empowerment paths:

1.3.3.1 Dual-Drive Construction Models

Panoramic data system construction features two viable models: "platform-centric" and "data-centric." The former emphasizes integration and immediate services, while the latter focuses on depth and open empowerment. University libraries can choose or blend these models based on their resources, technical capabilities, and service objectives.

1.3.3.2 Diversified Empowerment Paths

Empowerment paths are by no means singular. The PKU model represents "direct empowerment" by lowering usage thresholds through integrated platforms, while the CBDB model represents "indirect empowerment" by stimulating global scholars' innovative vitality through high-quality foundational data. Both suit different goals: embedding internal teaching and research versus expanding external academic influence.

1.3.3.3 Common Cornerstones of Sustainability

Regardless of model, continuous data governance, clear property rights definition, and stable community maintenance are indispensable foundations for success. CBDB's decades-long accumulation and PKU Laboratory's sustained investment both confirm that such projects are "long-termism" endeavors rather than short-term initiatives.

In conclusion, these two cases validate the feasibility and diversity of moving from "resource repository" to "smart services" from different perspectives. They jointly indicate that the success of university libraries' panoramic data systems for Chinese excellent traditional culture hinges on identifying precise academic entry points and providing indispensable "digital infrastructure" for humanities research through high-quality data organization and an open service mindset.

2. From "Resource Repository" to "Smart Services": The Evolution of University Libraries' Role

2.1 Traditional Role: Limitations of the "Resource Repository" Model

The traditional model is physical-carrier-centered, emphasizing collection over utilization; digital resources exist in "silo" states without interconnection; services remain passive, limited to consultation and circulation, with insufficient deep knowledge mining.

2.2 Transformation Drivers: Triple Forces of Technology, Demand, and Policy

Digital technologies have dissolved the boundaries between resource storage and utilization; university faculty, students, and the public increasingly demand immersive, interactive, and knowledge-intensive cultural services; national policies guide libraries to become important platforms for cultural inheritance and innovation.

2.3 New Positioning: Core Connotation of "Smart Services"

Datafication: Transforming resources into structured data that is computable and analyzable.
Contextualization: Embedding services into specific scenarios of teaching, research, learning, and cultural experience.
Intelligentization: Utilizing AI technologies to provide value-added services such as personalized recommendations, knowledge Q&A, and deep analysis.
Openness: Adhering to FAIR principles (Findable, Accessible, Interoperable, Reusable) to promote data sharing and reuse.

3. Connotation and Construction Model of the Panoramic Data System for Chinese Excellent Traditional Culture

3.1 Core Connotation of the "Panoramic Data System"

The "panoramic data system" is not a simple application of single technology but a modern data governance and service paradigm guided by systematic thinking, aiming for comprehensive, multidimensional, and deep-level revelation and integration of Chinese excellent traditional culture resources. Its core lies in breaking the isolated, static limitations of traditional resource repositories and building a complete ecosystem with comprehensive elements, tight associations, full lifecycle coverage, and rich experiences through digital technology empowerment.

3.1.1 Full Elements: Deep Integration of Multimodal Resources

The primary characteristic of the panoramic data system is the comprehensiveness of resource types. It transcends traditional text digitization to incorporate various cultural carriers, forming a multimodal resource complex. This includes: textual resources such as ancient books, local gazetteers, and archives; image resources such as paintings, murals, and rubbings; audio resources such as operas, folk songs, and oral histories; video resources such as traditional crafts and ritual activities; and 3D model resources for cultural relics like bronze ware, ceramics, and ancient architecture. Multimodal fusion forms the foundation for high-fidelity preservation and creative representation of cultural heritage, providing a data cornerstone for subsequent deep knowledge discovery and experiential innovation [12].

3.1.2 Full Association: Deep Construction of Knowledge Networks

"Full association" refers to using modern information technology, particularly knowledge graph technology, to deeply reveal and formally express complex semantic relationships within and between cultural resources. It aims to connect originally isolated resource nodes (e.g., persons, events, locations, time periods, works, concepts) into a semantically rich knowledge network. By defining and instantiating relational predicates such as "studied under," "occurred at," "created," "influenced," and "inscribed," the system can explicitly present spatiotemporal, social, causal, and influence relationships among cultural elements, thereby supporting semantic-based intelligent retrieval, knowledge reasoning, and associative discovery [13].

3.1.3 Full Lifecycle: End-to-End Data Lifecycle Management

The panoramic data system addresses the entire process from data "birth" to "utilization," covering the complete lifecycle of digital collection, metadata description, knowledge organization, standardized management, long-term preservation, and innovative service. This means system construction includes not only front-end digitization but also emphasizes mid-stage standard formulation, quality control, data governance, and fusion, as well as back-end persistent storage and sustainable utilization mechanisms. Full lifecycle management ensures the standardization, usability, interoperability, and long-term value of data resources [14].

3.1.4 Full Experience: Seamless Interaction for Multi-Level Users

"Full experience" emphasizes user-centric design, leveraging the panoramic data system to provide differentiated, immersive cultural experiences for diverse user groups ranging from professional researchers to the general public. For scholars, it offers precise data retrieval, associative analysis, and visualization tools for in-depth research; for the general public, it provides immersive popular science, interactive exhibitions, and online experiences based on augmented reality (AR), virtual reality (VR), and gamified narratives, achieving popularized and entertaining dissemination of cultural knowledge to empower social education and cultural inheritance [15].

3.2 Construction Model: Four-Layer Coordinated Framework

Based on the above connotation, this study proposes a four-layer coordinated construction model comprising resource, data, platform, and service layers. This model progresses layer by layer with mutual support, jointly forming the implementation path for the panoramic data system.

3.2.1 Resource Layer (Foundation) [16]

Content Sources:
- Endogenous resources: Core collections of university libraries, such as rare ancient books, stone rubbings, local gazetteers, Republican-era publications, and special archives.
- Cooperative resources: Cultural relic information, archaeological data, and intangible cultural heritage materials integrated through partnerships with museums, archives, and cultural research institutes.
- Network resources: Publicly available digital resources, folk art materials, and relevant research findings scattered across the internet, collected through web crawling and subjected to copyright screening and standardization.

Key Technologies:
- High-precision scanning and non-linear reproduction for non-contact digitization of precious documents.
- 3D laser scanning and photogrammetry for 3D modeling of cultural relics and ancient architecture.
- OCR (Optical Character Recognition) and HTR (Handwritten Text Recognition) for converting image text into computable structured text.
- Audio-video digitization and restoration for rescuing and enhancing old records, tapes, and films.

3.2.2 Data Layer (Core) [17]

Data Processing:
- Metadata standard formulation: Designing metadata schemes suitable for multimodal traditional culture resources by following and extending international standards (e.g., Dublin Core), domestic standards (e.g., "Chinese Metadata Scheme"), and domain standards (e.g., CADAL project metadata specifications).
- Information extraction: Utilizing natural language processing (NLP) technologies, particularly named entity recognition (NER), relation extraction (RE), and event extraction (EE), to automatically extract key knowledge units such as persons, places, official positions, events, and works from textual resources.
- Data cleaning and standardization: Establishing authority control files (e.g., name authority files, place name authority files) to clean, normalize, and link heterogeneous extracted data, ensuring consistency.

Knowledge Organization:
- Ontology construction: Building a "Chinese Excellent Traditional Culture Ontology" that defines core concepts (e.g., "Person," "Work," "Event," "Location," "Concept") and their semantic relationships (e.g., "isCreatedBy," "happenedAt," "isPartOf," "influenced") to provide schema-level constraints for knowledge graphs.
- Knowledge graph construction and storage: Instantiating processed data into the ontology model to form large-scale knowledge graphs, stored and managed efficiently using graph databases (e.g., Neo4j, Nebula Graph).

3.2.3 Platform Layer (Support) [18]

Core Functions: Providing a technical middle platform that integrates massive data storage, high-performance computing, intelligent analysis, and multidimensional visualization to support upper-layer applications.

Key Components:
- Distributed storage systems: Such as HDFS and Ceph, for storing massive unstructured original resources (images, videos, 3D models) and structured data.
- Data middle platform/computation engines: Providing data integration, processing, analysis, and API encapsulation capabilities, such as using Spark and Flink for batch and stream computing.
- Unified API interfaces: Opening data and service capabilities to third-party systems or front-end applications through RESTful APIs.
- Visualization toolkits: Integrating or developing diverse visualization tools including timelines, geographic information systems (GIS), social network analysis (SNA) graphs, and 3D displays to transform data into intuitive visual insights.

3.2.4 Service Layer (Objective) [19]

The service layer represents the ultimate value of the panoramic data system, aiming to provide precise and efficient smart service interfaces for different user groups.

For Research Users:
- Knowledge discovery systems offering complex semantic retrieval, associative path discovery, and knowledge reasoning.
- Data analysis tools providing online research tools for social network analysis, spatiotemporal evolution analysis, and text mining.
- Data open interfaces providing clean, standardized datasets and APIs for digital humanities researchers to support quantitative analysis.

For Public Users:
- Immersive experience applications developed based on VR/AR, including virtual exhibitions, cultural site reconstructions, and interactive games.
- Personalized recommendation services based on user behavior profiling.
- Creative empowerment platforms providing resource downloads and secondary creation tools to support cultural creative product development (e.g., digital cultural creations).

For Librarians:
- Data cockpits providing backend management functions for data monitoring, resource usage statistics, and service effect evaluation to support scientific decision-making.

4. Research on the Empowerment Path of the Panoramic Data System

The ultimate value of constructing the panoramic data system for Chinese excellent traditional culture lies in its "empowerment" effect. This chapter systematically elaborates how the panoramic data system transforms traditional service models from the bottom up across four core dimensions: research innovation, education, cultural dissemination, and public engagement, thereby achieving strategic upgrading of university library functions. The overall empowerment logic can be summarized as the evolutionary path from data resourceization to resource knowledgeization, knowledge servitization, and service intelligentization. The following table illustrates the overall framework of the panoramic data system empowerment path.

[TABLE:1] Data Processing Flow and Empowerment Application Table

(Table of Data Processing, Knowledge Graph Development, and Application Enablement with Corresponding Goals)

Empowerment Application Direction Data Resourceization (Bottom-Level Data Integration) Resource Knowledgeization (Mid-Level Knowledge Construction) Service Intelligentization (Top-Level Application Empowerment) Multi-source heterogeneous data aggregation Data cleaning and standardization Metadata extraction and normalization Entity recognition and semantic indexing Knowledge extraction and association Building domain knowledge graphs Intelligent retrieval and knowledge discovery (As foundational support for all upper-layer applications) Forming trustworthy, usable, manageable high-quality data assets (As core driver for all smart services) Forming semantically interconnected, inferable structured knowledge networks Visualization analysis and contextual computing Empowering research innovation Driving paradigm transformation in data-driven research API interfaces and service packaging Empowering education Creating immersive, interactive smart classrooms Empowering cultural dissemination Achieving precise, interactive living transmission Empowering public engagement Promoting equitable, open, and shared cultural resources

Note: This table systematically outlines the complete value realization path from bottom-level data to top-level empowerment in the panoramic data system for Chinese excellent traditional culture, drawing on the Data-Information-Knowledge-Wisdom (DIKW) model applied in the digital humanities domain [20][21].

4.1.1 Vertical Process Evolution

The table follows the progressive logic of "Data Resourceization → Resource Knowledgeization → Service Intelligentization." This process clearly demonstrates the evolution from multi-source heterogeneous raw data, through processing and integration into high-quality data assets, then elevation through knowledge graph technology into structured knowledge systems, and finally encapsulation as smart services empowering different application scenarios, embodying the core path of library intelligent transformation from basic resources to smart services [22].

4.1.2 Clear Hierarchical Functions

Data resourceization is the foundation, focusing on aggregation and cleaning to address data "usability" and "trustworthiness," forming a reliable cornerstone for subsequent knowledgeization [23]. Resource knowledgeization is the core, with the key being the construction of domain knowledge graphs to achieve semantic association and organization, transforming discrete resources into machine-understandable, inferable structured knowledge networks [24]. Service intelligentization is the objective, emphasizing the use of intelligent technologies (e.g., contextual computing, visualization analysis) to encapsulate knowledge into usable services that directly embed into users' research, learning, and experience scenarios, realizing the transformation from "people seeking knowledge" to "knowledge finding people" [25].

4.1.3 Clear Empowerment Paths

At the "service intelligentization" level, the system delivers precise empowerment to four key directions through a unified smart service engine. These four paths not only respond to university libraries' core missions of serving teaching and research and inheriting innovative culture, but also align with the internal requirements of the national cultural digitalization strategy regarding promoting inclusive public services and shared cultural achievements [26].

4.2 Empowering Research Innovation: From "Data Query" to "Paradigm Revolution"

A core empowerment of the panoramic data system lies in promoting the digital transformation of humanities and social science research paradigms, shifting from the traditional "close reading" dependent on individual reading and speculation to a new "digital humanities" paradigm combining data-driven "distant reading," quantitative analysis, and visual exploration [27].

4.2.1 Path: Providing Data-Driven Research Paradigms and Infrastructure

University libraries should transform from "data providers" to "builders and service providers of research infrastructure." Specific paths include: (1) Providing digital humanities toolsets by integrating or developing digital tools suitable for traditional culture research on the panoramic data platform, such as text mining tools (for word frequency analysis, sentiment analysis, topic modeling), social network analysis tools (for analyzing interpersonal relationships), and spatiotemporal visualization tools (GIS), offering researchers a "methodological toolbox" [28]. (2) Building computable knowledge resources by deeply associating and semantically annotating entities like classics, persons, events, locations, and concepts through knowledge graphs to form structured, machine-understandable knowledge networks that lay the foundation for discovery-based research on linked data.

4.2.2 Case Demonstrations

Case 1: Scholarly Network Discovery Based on Knowledge Graphs
Using the "Tang-Song Literati Knowledge Graph" constructed in the panoramic data system, researchers can query the social networks of core figures like Li Bai and Du Fu. Through social network analysis algorithms, the system can automatically calculate and visualize key nodes (social centers), different literati groups (community detection), and even identify "bridge-type" figures overlooked in traditional literary history who may have played crucial roles in information transmission between groups [29].

Case 2: GIS Analysis of Geographic Imagery Distribution in Classical Poetry
By extracting and geocoding all place names mentioned in the "Complete Tang Poems" and mapping them onto GIS, researchers can macroscopically analyze the geographic centers of Tang poets' activities, thematic differences in poetry creation across regions (e.g., frontier poems concentrated in the northwest, pastoral poems in the Central Plains), and the influence of major transportation routes (e.g., Grand Canal, Silk Road) on literary dissemination, achieving macro-level, quantitative literary geography research [30].

4.3 Empowering Education: From "Supplementary Materials" to "Immersive Classrooms"

The panoramic data system can transform static knowledge points into interactive, explorable three-dimensional teaching resources, shifting teaching models from "teacher-centered" knowledge transmission to "student-centered" inquiry-based and experiential learning.

4.3.1 Path: Developing Smart Educational Resources Deeply Integrated into Teaching Processes

Thematic Teaching Resource Packages: Around specific teaching themes (e.g., "Song Dynasty Social Life," "Silk Road"), the system extracts relevant digital classics, images, audio-video, maps, and research findings from the panoramic data system to package into structured teaching resource bundles for direct classroom use or student self-learning.

Virtual Simulation "Golden Courses": Using VR/AR technology to create high-fidelity historical scenes based on authentic historical data. For example, students can "walk into" virtual Han Dynasty Chang'an or Song Dynasty Bianjing to intuitively understand urban layouts, architectural styles, and social landscapes, achieving "contextualized teaching" [31].

Course-Embedded Services: Librarians collaborate with subject teachers to integrate the use of the panoramic data platform and its tools as part of course assignments, directly cultivating students' information literacy and digital humanities research capabilities.

4.3.2 Case Demonstration

Practice in "Ancient Chinese History" and "Tang Poetry Appreciation" Courses: In "Ancient Chinese History," teachers assign group projects requiring students to use historical map bases and GIS tools provided by the panoramic data platform to collaboratively map territorial changes, population migrations, or war routes during specific historical periods (e.g., before and after the An Lushan Rebellion). In "Tang Poetry Appreciation," students select a poet and use the platform's trajectory data, chronological poems, and GIS tools to map their "life trajectory" and analyze the relationship between life experiences and poetic style evolution [31].

4.4 Empowering Cultural Dissemination: From "Static Exhibitions" to "Dynamic Experiences"

The panoramic data system is key to breaking the "aloof" image of traditional culture and achieving "circle-breaking" dissemination. Its goal is to transform cultural communication from one-way, static transmission to two-way, dynamic, participatory experiences.

4.4.1 Path: Creating Online-Offline Integrated Immersive Cultural Experience Fields

Online Virtual Exhibition Halls: Building never-closing online 3D virtual exhibition halls based on panoramic data, where users can freely roam, click exhibits to view high-definition details and in-depth interpretations, far exceeding the space and content limitations of physical exhibitions.

AR/VR Interactive Experiences: Developing mobile AR applications where users scanning specific images or scenes can see cultural relic restoration or historical scene overlays on their screens. VR experiences allow users to "travel" into the street scenes of "Along the River During the Qingming Festival" and "interact" with figures in the painting, deeply experiencing Song Dynasty urban life.

Social Media Light Applications: Developing fun, easily shareable lightweight applications such as "AI Classical Poetry Matching," "Famous Painting DIY Puzzles," and "Traditional Costume Dress-up H5" to achieve fun and social dissemination of culture in ways that resonate with younger audiences [32].

4.4.2 Objectives

To make cultural relics and classics "come alive," attract Generation Z audiences, and enhance cultural identity and pride.

4.5 Empowering Public Engagement: From "In-House Services" to "Open Sharing"

As important components of the public cultural service system, university libraries have the responsibility to open valuable cultural data resources to society and stimulate innovation vitality across all sectors.

4.5.1 Path: Building Open Data Platforms and Innovation Ecosystems

Open Data Policies: Formulating clear data openness tiering strategies to maximize the release of non-confidential metadata and partial raw data to the public, primary and secondary schools, cultural creative enterprises, and research institutions while protecting intellectual property rights and privacy.

Providing API Interfaces: Offering standardized application programming interfaces that allow third-party developers to access data and develop diverse applications such as educational apps, cultural creative products, and data analysis tools. This shifts the paradigm from "libraries provide what the public uses" to "the public creates what they need using data."

Hosting Data Innovation Competitions: Attracting talent from all sectors to solve practical problems or create cultural products using open data through "cultural data hackathons" or innovation contests, fostering an innovation ecosystem around traditional culture data.

The European "European Data Portal" and China's National Center for Philosophy and Social Sciences Documentation practices provide reference examples for data openness and sharing, emphasizing the value of open data in promoting innovation and economic growth [33].

This chapter systematically discusses empowerment paths across four dimensions. Essentially, through the fusion of data and technology, it breaks spatiotemporal and media limitations to transform traditional culture resources into computable, experiential, and re-creatable smart capital, ultimately driving university libraries' evolution from guardians of resource repositories to smart service hubs that activate cultural vitality.

5. Challenges and Countermeasures

5.1 Major Challenges

Standards and Interoperability Challenges: Inconsistent metadata standards hinder data fusion.
Copyright and Ethical Challenges: Issues include ancient book digitization copyrights and personal privacy (e.g., historical figure data).
Technology and Talent Challenges: Requires interdisciplinary talent proficient in both library science and IT, with high technical thresholds.
Sustainability Challenges: High initial investment with continuous funding and human resources needed for later maintenance and updates.

5.2 Countermeasures

Strengthen top-level design by participating in or formulating industry-wide unified standards; establish reasonable copyright risk avoidance mechanisms and ethical review systems; enhance librarian training, introduce external technical teams, and conduct interdisciplinary projects; secure special university funding and explore project result transformation and sustainable operation models.

6. Conclusion

In summary, constructing the panoramic data system for Chinese excellent traditional culture is key to university libraries' transformation from "resource repositories" to "smart services." The proposed "four-layer coordinated" construction model and "four-in-one" empowerment path demonstrate effectiveness and innovation.

6.2.1 Deepening Technology Integration

AIGC (AI-Generated Content) can be used for automatic indexing, content summarization, intelligent Q&A, and even creative generation, significantly improving efficiency.

6.2.2 Metaverse Applications

Libraries may construct a "Chinese Culture Metaverse," providing unprecedented immersive interactions.

6.2.3 Ecosystem Co-Construction

Moving from single-library construction to nationwide library consortium co-construction and sharing, forming genuine nodes of a "National Cultural Big Data System."

7. Conclusion

This study systematically explores how university libraries can evolve from traditional "resource repositories" to innovative "smart services" by constructing a panoramic data system for Chinese excellent traditional culture. The article's proposed "resource-data-platform-service" four-layer coordinated construction model and diversified empowerment paths for research, teaching, dissemination, and public engagement provide a clear theoretical framework and practical guide for this transformation.

The research confirms that the core of building a panoramic data system lies in achieving the sublimation from "digital resources" to "smart data." Its value extends beyond technical implementation to profoundly reshaping humanities research paradigms, educational models, and cultural dissemination forms. Through knowledge graphs, intelligent technologies, and open collaboration, dormant collection resources are activated and transformed into smart sources driving academic innovation and cultural inheritance.

Looking forward, with the deep integration of generative AI, metaverse, and other technologies, the connotation and extension of panoramic data systems will continue expanding. University libraries should seize opportunities, consolidate data foundations while actively exploring smarter and more immersive service frontiers, ultimately becoming core drivers leading the creative transformation and innovative development of Chinese excellent traditional culture in the digital age.

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Author Bio: Zhou Taotao, Associate Research Librarian, Bachelor's degree, Email: 165653958@qq.com.