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Aiming the situation that rolling bearing in noisy and load changes at the actual working environment, A bearing fault diagnosis method based on Double attention convolution mechanism ResNet (DACM_ResNet) is proposed. First, short-time Fourier transform (STFT) is performed on the vibration signal of the rolling bearing and pseudo-color processing is used to obtain three-channel image data. Then, the residual neural network is studied on bearing fault diagnosis. After the convolution layer of the residual block, the DACM module is used to further extract the residual features from the two dimensions of channel and space, and the connection between the residual and the input is established. Finally, experiments were carried out on the Case Western Reserve University (CWRU) dataset, and the test results show that the proposed method has an average accuracy of more than 98% under the noise environment and load changes, indicating that the proposed model has good noise resistance