An intelligent fault diagnosis method for spiral bevel gear box based on continuous wavelet transform (CWT) and coordinate attention mechanism residual network (CooAtten-Resnet) is proposed. Firstly, a large number of signal samples are obtained by overlapping sampling of vibration signals. These samples are converted into time-frequency maps by continuous wavelet transform, and time-frequency data sets under different faults are constructed. At the same time, noise samples are added manually to verify the impact of noise on such diagnostic methods; Then the time-frequency map data set is used for CooAtten-Resnet training; Finally, the fault is classified and the diagnosis results are output. The results show that this method can accurately identify the fault of spiral bevel gear box, and the accuracy rate of diagnosis can reach 100% when no one adds noise, and the accuracy rate is still above 93% when no noise reduction is conducted after adding noise. Compared with other methods, this method has higher accuracy, stronger anti-noise ability, faster network convergence and more stable diagnosis results.