The vibration signal during the operation of high voltage circuit breaker can reflect the mechanical state of circuit breaker. Aiming at the shortcomings of feature extraction and fault diagnosis accuracy of shallow vibration signal analysis model, a fault diagnosis method of high voltage circuit breaker based on convolutional neural network optimized by genetic algorithm was proposed. Using the global optimization ability of genetic algorithm, the optimal initial network structure parameters and the number of neurons in the whole connection layer were obtained through the selection, crossover and mutation of genetic algorithm to optimize the convolutional neural network, and the optimized convolutional neural network is applied to the fault diagnosis of high voltage circuit breaker. The results show that the diagnosis performance of the proposed network model is better than that of convolution neural network, dynamic support vector machine and multilayer perceptron.