Aiming at the problem of easy interference of electrostatic signal and low fault recognition rate when the new electrostatic monitoring technology is applied to rolling bearing fault diagnosis, a method of electrostatic signal recognition of rolling bearing fault based on the combination of Bayesian optimization SVM is proposed. First of all, through the electrostatic simulation test platform constructed, the electrostatic signals of different wear states of bearings under high speed are collected, and the feature sets of different working conditions are selected according to the time-domain feature parameters; and then the hyper-parameters of the minimum error of SVM are selected using Bayesian optimization to achieve the effect of completing the diagnostic model training, and the diagnostic accuracy of the models is evaluated with the results of the confusion matrix after training. The research results show that this method has certain recognition ability for bearings with different fault characteristics under electrostatic monitoring, and the Bayesian optimization algorithm can effectively improve the recognition efficiency, and its average recognition accuracy can reach 98.82%.