To address the issues associated with traditional methods for mechanical fault diagnosis, such as difficulties in feature extraction and complex classifier training, we proposed a rolling bearing fault diagnosis method based on S-transform and the convolutional neural network (CNN). First, the original data of the bearing were subjected to S-transform to obtain a time-frequency image. Then, secondary feature extraction was performed using the CNN. Next, fault classification was conducted using the classifier and the fault diagnosis of the rolling bearing was performed. Experimental results show that compared with long short-term memory networks, CNN, and support vector machine, the proposed method achieves higher diagnostic accuracy and better stability.