Bearing fault diagnosis plays a vital role in maintaining rotating machinery and avoiding major disasters. Given that the existing fault diagnosis model cannot adapt to the changing working loads in actual industrial applications, a fault diagnosis method based on feature fusion and hybrid enhancement is proposed. For this purpose, new feature signals are generated by fusing time-frequency features, working condition features, and time difference features into the original signal. Then, the phase space reconstruction theory is applied to convert the feature signals into image signals, and data distribution is expanded through hybrid enhancement during training. Finally, the residual network is used for fault diagnosis analysis. The experimental results on the Case Western Reserve University (CWRU) dataset show that the prediction accuracy of this method under invariable working conditions is up to 100% and its average prediction accuracy under changing working conditions reaches 93.28%, which indicates that the proposed method has a remarkable domain adaptability.