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In order to diagnose the bearing fault simply and accurately, combined with deep learning theory, a mode based on convolutional neural network(CNN) was proposed. Firstly, the LeNet5 CNN with simple model architecture was chosed;Secondly, using the raw data of the bearing vibration signal which is intercepted and normalized, a two-dimensional matrix is generated directly as the input of CNN; Thirdly, the convolution, kernel batch, learning ratesize and the iterations was optimized. Finally, the sigmoid function was choiced to classify. The experimental results show that the method can identify the normal, inner ring fault, outer ring fault and rolling fault effectively and the recognition accuracy can reach a level of over 99.50%. Bearing fault diagnosis algorithm based on convolutional neural network not only simplifies the process of fault diagnosis to a certain extent, but also fully utilizes the advantages of CNN models to achieve efficient and accurate fault diagnosis.