Aiming at the problem that the mechanical faults of high-voltage vacuum circuit breakers are difficult to identify, a high-voltage vacuum circuit breaker fault diagnosis method based on Ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) is proposed. Use the laboratory 10kV indoor vacuum high voltage circuit breaker to collect the vibration data of 9 states such as normal opening and closing actions, loose screws, falling screws, jammed transmission mechanism, insufficient closing spring energy storage, etc., and solve the collected data. The energy entropy value of the EEMD component constitutes a feature vector set. The feature vector set is divided into a training set and a test set. The training set is used to train the SVM to obtain an intelligent fault recognition model. The test set is input to the model for testing to realize the high-voltage vacuum circuit breaker mechanism. Troubleshooting. Experiments show that the mechanical fault diagnosis method of high-voltage vacuum circuit breakers based on EEMD and SVM can effectively identify mechanical faults in different states.