Aiming at the characteristics of complex features in bearing fault signals, a bearing fault determination method combining the attention mechanism and multi-scale residual convolutional network is proposed. The model combines the powerful feature extraction capability of convolutional neural network (CNN) and the adaptive weighting capability of the attention mechanism, which can effectively deal with the complex features in the bearing fault signal. The model employs a multi-scale convolutional layer, which captures the multi-scale features of the signal through different sizes of convolutional kernels, which helps to recognize different types and severities of faults. Meanwhile, the residual structure is introduced to effectively integrate the features extracted by multilayer convolution through the cooperative decision-making mechanism of high-dimensional and low-dimensional features, which enhances the model′s ability to perceive the key information and reduces the problems of gradient vanishing and feature redundancy in the training of the deep network, so as to ensure the stability and accuracy of the model. The fusion of attention mechanisms (e.g., SEBlock and ECABlock) enables the model to adaptively focus on more important feature channels, which further improves the diagnostic performance. The experimental results show that the model can achieve high-precision diagnosis under various fault modes, demonstrating its potential application in intelligent maintenance and fault warning systems.