To address the complex feature extraction problem in traditional rolling bearing fault diagnosis, an end-to-end rolling bearing intelligent fault diagnosis method based on a multi-attention mechanism is proposed by introducing channel attention and spatial attention mechanism using the feature that deep residual network can enhance the nonlinear characterization ability of the diagnosis model. Firstly, the vibration velocity and displacement signals are obtained by integrating the original vibration acceleration signal. Secondly, the three types of signals are combined into an image with feature enhancement and input to a deep residual network combined with a multi-attention mechanism for feature extraction. Finally, a multi-classification function is used to complete the rolling bearing fault classification. The validation was carried out on a local laboratory-bearing dataset, and the results showed that the diagnostic accuracy of the proposed method reached 97.50%. The feasibility and effectiveness of the end-to-end rolling bearing intelligent fault diagnosis method based on a multi-attention mechanism are verified, which can support the accurate fault diagnosis of rolling bearings.