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Bearing fault diagnosis in the actual industrial scene, there are some problems, such as the lack of bearing fault samples, and the distribution difference between the training samples and the actual signal samples. A new fault diagnosis method based on deep transfer auto-encoder is proposed in this paper, which is applied to the fault diagnosis of bearings under different working conditions. Firstly, wavelet packet transform is used for signal processing and feature extraction; Secondly, the proposed feature selection method based on Naive Bayes and difference between domains (FSBD) is used to evaluate the statistical features and select the features that are more conducive to cross-domain fault diagnosis and transfer learning; the source domain feature data is used to train the deep auto-encoder, and parameters of the trained model are migrated to the target domain. Then, the normal state samples of the target domain are used to fine-tune the deep transfer auto-encoder model, and the fine-tuned model is used for fault classification of the target domain unlabeled feature data. Finally, based on the CWRU bearing fault data, fault diagnosis experiments under different working conditions are performed. The results show that the proposed FS-DTAE method can effectively improve the fault diagnosis accuracy under different working conditions.