Intermittent motion equipment is a critical component of intelligent logistics systems, and its operational condition directly impacts the safety and reliability of the entire system. In view of the complexity and uncertainty inherent in the operation of bearings within intermittent motion equipment, and the challenges posed by difficulties in acquiring effective data and the scarcity of fault samples under complex operating conditions, which result in low accuracy in rolling bearing fault diagnosis, this study proposes a fault diagnosis method for intermittent motion equipment under complex operating conditions based on the SResNet network. Firstly, an intermittent operating condition recognition method is proposed to enhance the effectiveness of state data. Secondly, the one-dimensional state data, after preprocessing, is transformed into a two-dimensional time-frequency spectrum using continuous wavelet transform (CWT), thereby enriching the fault feature information. Finally, an improved ResNet50 network is developed by incorporating a self-attention module (SAM). The SAM enhances the network’s focus on important features, thereby improving the accuracy and stability of bearing fault diagnosis. To validate the fault diagnosis performance of the proposed method, experiments were conducted using a bearing fault state simulation dataset. The results demonstrate that, under complex operating conditions, the proposed method can accurately classify and identify bearing faults, achieving a classification accuracy of over 99%. Compared to traditional fault diagnosis methods, the proposed approach exhibits significant improvements in diagnostic performance and generalization capability.