Aiming at the problem that rolling bearings are difficult to identify faults in noisy environments, a rolling bearing fault judgment method combining attention mechanism and Inception-ResNet is proposed. Firstly, a method combining grayscale image and pseudo-color processing is proposed to convert one-dimensional vibration signal into three-dimensional RGB image; then combined with Inception module and residual network, the network is expanded in both width and depth, and the network is improved. Finally, combined with the CBAM attention mechanism, the channel attention module and the spatial attention module are integrated to enhance the more important features of the input features and suppress unnecessary noise features, thereby effectively improving the diagnostic accuracy. In this paper, the bearing data set of Case Western Reserve University is used for verification, and several mainstream deep learning methods are selected for comparative experiments. The test results show that this method has a good diagnostic accuracy rate, the average accuracy rate is as high as 99.32%. The analysis experiment is carried out under the noise state, and the results show that the method still has a good accuracy rate under the noise state, which verifies the robustness of this method.