[Objective] To address the challenges of insufficient feature extraction, inadequate capture of temporal dependencies, and low diagnostic accuracy under noise interference in motor fault diagnosis under complex working conditions, a fault diagnosis method based on a multi-scale convolutional neural network, long short-term memory, and attention mechanism (MCNN-LSTM-Attention) is proposed. [Methods] First, through the MCNN module, the parallel convolutional layers were used to extract multi-scale local features from the vibration signals, addressing the problem of difficulty in comprehensively characterizing fault features. Then, the LSTM module was utilized to perform temporal modeling on the multi-scale feature sequences, deeply mining the temporal dependencies of the fault features. On this basis, the Attention mechanism was introduced to adaptively weight the temporal features, so as to focus on the key fault information and suppress irrelevant noise interference. Finally, the multiple modules worked collaboratively to solve the problems of incomplete feature representation and weak robustness of the diagnostic model. [Results] To comprehensively evaluate the robustness and adaptability of the proposed motor fault diagnosis method in practical applications, typical non-ideal factors such as load fluctuations and speed disturbances were simulated and introduced by emulating complex on-site operating conditions, and noise interferences of different intensities were superimposed during the signal acquisition process, so as to construct a test scenario close to the real industrial environment. On this basis, the diagnostic model was verified using actual collected motor operating data. The experimental results showed that the method maintained high diagnostic accuracy under various complex operating conditions and different noise levels, and at the same time, it exhibited good generalization capability in tests across different operating conditions and fault types. [Conclusion] The proposed method achieves accurate fault diagnosis of motors under complex operating conditions. This result provides reliable technical support for practical engineering applications, and also offers useful reference and new insights for preventive maintenance measures in subsequent equipment operation.