Aiming at the low accuracy of high-voltage circuit breaker fault diagnosis under small samples and complex working conditions, a fault diagnosis method of high-voltage circuit breaker based on vibration signal processing and AdaBoost ensemble learning is proposed. First, the high-voltage circuit breaker test platform is built and the switching vibration signals are collected under 8 working conditions. Second, after absolute value processing of vibration signals, piecewise aggregate approximation (PAA) is used to do piecewise averaging, and Gramian angular field (GAF) is used to convert new output sequences into pictures. The Relief F method is used to sort the importance of the extracted high-dimensional image features. Finally, the retained important features are input into the AdaBoost ensemble learning model for fault diagnosis, and the snake optimization algorithm is used to determine the optimal PAA step size and the number of input classifier features to further improve the fault diagnosis accuracy. The comparison and analysis results with various signal processing methods and classification models indicate that picture signal and AdaBoost ensemble learning model can deal with vibration signal effectively and judge fault type accurately, which provides a new way to diagnose high-voltage circuit breaker fault accurately and reliably.