In order to improve the accuracy of bearing fault diagnosis, the depth convolution neural network algorithm is used to realize bearing fault classification. Firstly, the bearing fault database is established according to the characteristic frequency of bearing vibration fault, then the vibration signal of bearing is periodically extracted according to different slice length and fixed width, and the feature vector matrix is established. Then the fault diagnosis model of deep convolution neural network is established. In the network design, the convolution kernel and pool size are set differently, and the core parameters of neural network training are optimized. Finally, a stable convolution neural network model is obtained. The simulation results show that the bearing fault classification based on deep convolution neural network has high accuracy and small standard deviation.