The invention discloses a fault diagnosis method for a high-voltage circuit breaker based on a neural network, and the method comprises the steps: S1, collecting the current, vibration and mechanical travel data and upper and lower opening distance point data of the high-voltage circuit breaker in a normal, shaft-breaking and under-voltage state during switching-on and switching-off; S2, data preprocessing, including vibration data preprocessing and current data processing; S3, taking the vibration characteristics, the current characteristics, the mechanical characteristics and the corresponding fault labels in the opening and closing process of the high-voltage circuit breaker as a data set, randomly selecting 80% of samples in the data set as a training set, taking the remaining samples as a test set, and randomly selecting 20% of samples in the current training set as a verification set; S4, through wavelet neural network and the Wide& Deep model, performing model training, and obtaining an optimal high-voltage circuit breaker fault pre-diagnosis model through the verification set; S5, testing the high-voltage circuit breaker fault pre-diagnosis model by using the test set, and evaluating the accuracy of the fault pre-diagnosis model.