Abstract: To solve the problem that the existing residual current protection device was difficult to identify electric shock accidents, a new method of electric shock identification based on wavelet high-frequency distribution characteristics and probabilistic neural network（PNN） was proposed. The S-transform was used to analyze the spectral characteristics of the residual current signal including the moment of electric shock, which was found that the high-frequency component of the residual current signal at the time of electric shock had sudden change in amplitude. The wavelet high-frequency distribution of each layer was extracted through the multi-scale frequency window provided by the wavelet multi-resolution analysis. To describe the electric shock accident, the normalized processing of each layer of wavelet high-frequency distribution mutations was used to accumulate and quantify the high-frequency characteristics of the first 5 layers of the residual current signal. Taking full account of the randomness of the time of electric shock accidents, the extracted wavelet features were divided into categories. A PNN-based electric shock accident recognition model was constructed, and the network smoothing parameters in the defined domain were optimized according to the specified step. The mean clustering method was also used to optimize the network structure. The results show that there is significantly sudden change in the amplitude of the residual current signal above 500 Hz at the time of electric shock. After normalization, the cumulative sum of the amplitude mutations of the wavelet high-frequency distribution of each layer can well describe the amplitude sudden increase in the corresponding stage of the wavelet high-frequency distribution of each layer. The optimal smoothing parameter interval of the established PNN network model is from 0.15 to 0.29, and the best corresponding identification rate of electric shock accidents is 95.5%.