The identification of partial discharge type of switchgear has important guiding significance for understanding the insulation state and timely maintenance. The key to partial discharge type identification is to extract the characteristics of the partial discharge signal. A feature extraction method for partial discharge ultrasonic signals combining Choi-Williams distribution and permutation entropy is proposed, the time-frequency characteristics of partial discharge ultrasonic signals are obtained by using Choi-Williams distribution, the permutational entropy of partial discharge ultrasonic signals is solved, the complexity feature quantity of signal time series is obtained, the time domain and complexity features are composed into feature vectors, and the BP neural network optimized by particle swarm optimization is used to classify and identify discharge signals. The measured data analysis shows that the accuracy of the method for the identification of discharge type reaches 96. 67%, which is 11. 67% and 1. 67% higher than the traditional fractal and timefrequency analysis methods, respectively.