The study of crystal structure is the basis for studying the physical and chemical properties of solid materials, and the screening of crystal structure is usually based on the principle of least energy. The use of density functional theory to calculate the structure energy requires a lot of computing resources and service time. For this reason, this research proposes a deep learning method for material structure prediction to speed up the prediction of material crystal structure. This work systematically studied and analyzed the data set optimization, training method, algorithm optimization, and so on. The network parameters and optimized algorithm of deep learning for crystal structure prediction are confirmed and coded. The optimized deep learning method is used to find out stable structure of Silicon, titanium dioxide, and perovskite CaTiO₃, the predicted structures are well agreement with the experimental results.