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Aiming at the problems of poor generalization ability and complex network of deep learning fault diagnosis model, a general feature extraction network is proposed, and the method of bearing fault diagnosis is applied on this basis. The frequency domain feature variational autoencoder is proposed for the first time, which enhances the robustness of signal feature extraction. Then, a local outlier algorithm is used to eliminate outliers, prevent the classifier from overfitting, and improve the generalization performance of the classifier. Finally, a classifier is constructed for fault diagnosis. The experimental verification shows that the boundary of feature extraction is clear under different damage degrees, the fault classification effect is good, and the model shows good transferability.