Abstract: In order to improve the accuracy of fault diagnosis of circuit breakers and realize accurate fault identification, this paper proposes a fault diagnosis method of high voltage circuit breakers (MI-ECHPO-PNN) based on mutual information feature selection and improved prey algorithm optimized probabilistic neural network. After the vibration signal is decomposed by variable mode decomposition, the components with higher frequency is selected to extract the fault feature, and the feature is screened by the mutual information algorithm as the input of the diagnosis model. Using the improved predator algorithm to optimize the smoothing factor of the probabilistic neural network, the optimized parameters are input into the probabilistic neural network to build an ECHPO-PNN fault diagnosis model. The simulation results show that the ECHPO-PNN model has better diagnostic effect than other PNN models do, and the accuracy can reach 100 %, showing good accuracy and stability.