Aiming at the shortcoming that the traditional fault diagnosis method of rolling bearings is difficult to extract the effective characteristics of bearing vibration data, a network model SPWVD-CNN based on smooth and pseudo Wigner-Vill distribution (SPWVD) and convolutional neural network(CNN) was proposed. It used smooth and pseudo Wigner-Vill distribution to analyze vibration signals of rolling bearings and Compressed the obtained timefrequency representations as input to CNN. Using the idea of migration learning to train the network, the model had good diagnostic performance for the data of different loads and improved the generalization ability of the network. The experimental results indicate that the average classification accuracy of SPWVD-CNN to bearing fault data is increased to 99.27%, and the overall performance is better than using a single CNN and other traditional fault diagnosis methods.