For gearboxes, variations in fault characteristics under different operating conditions, susceptibility to noise interference in fault diagnosis, lead to poor generalization and low recognition accuracy of fault diagnosis models. An end-to-end convolutional block attention module-sparse temporal convolutional network with soft thresholding(CBAM-STCN) was proposed for gearbox fault diagnosis. Firstly, the Hilbert transform was employed to convert the gear fault vibration signal into an envelope spectrum signal. Then, this signal was input into the CBAM-STCN fault diagnosis model. The model integrates a hybrid attention mechanism module, the convolutional block attention module (CBAM), which adaptively learns the weights of channel and spatial attention to extract information sensitive to fault features. The embedded soft thresholding function minimizes the discrepancy between the model's output and the original input. Finally, the proposed method was utilized to identify and classify various types of gear faults under two different conditions. The results indicate that the CBAM-STCN model achieves an average accuracy of 98.95% in intelligent gear fault diagnosis, demonstrating its potential value for gearbox fault diagnosis.