This paper proposes a numerical method for solving partial differential equations based on deep neural networks. By constructing a physics-informed neural network to approximate the solution of the equation, the accuracy and efficiency of the method are validated on multiple benchmark problems. Experimental results show that the method has significant advantages for high-dimensional problems.