This paper focuses on the difficulties that appear when the number of fault samples collected by a permanent magnet synchronous motor is too low and seriously unbalanced compared with the normal data. In order to effectively extract the fault characteristics of the motor and provide the basis for the subsequent fault mechanism and diagnosis method research, a permanent magnet synchronous motor fault feature extraction method based on variational auto-encoder (VAE) and improved generative adversarial network (GAN) is proposed in this paper. The VAE is used to extract fault features, combined with the GAN to extended data samples, and the two-dimensional features are extracted by means of mean and variance for visual analysis to measure the classification effect of the model on the features. Experimental results show that the method has good classification and generation capabilities to effectively extract the fault features of the motor and its accuracy is as high as 98.26%.