The convolutional neural network (CNN) identification method and the BP neural network identification method were used to diagnose the bearing fault respectively. When using the CNN diagnostic method, first perform continuous wavelet transform (CWT) on the vibration signal of the rolling bearing to obtain a time-frequency map, and then compress the time-frequency map to an appropriate size; Then, the compressed time-frequency map is used as a feature map to input into the CNN classifier model established; finally, an experimental study is carried out based on the artificial bearing fault data set of Western Reserve University. The results show that the average accuracy rate of this method is greater than 99%.In the BP fault diagnosis method, based on the data set, nine parameters of average value, maximum value, minimum value, peak-to-peak value, root mean square value, standard deviation, variance, skewness and kurtosis are established as training input vectors. Using BP neural network with 10 hidden layer nodes for fault identification, the results show that the average accuracy of identification is 93.7839%. The comparative analysis of the two methods shows that the BP identification method has higher training efficiency and takes less time; the CNN identification method has higher recognition accuracy, but the training takes more time.