The traditional PNN neural network has strong fault tolerance, simple learning process and fast training speed. Based on the traditional PNN neural network, this paper uses LMS to optimize its heart sound classification, and then improve heart sound classification and prediction. accuracy. The LMS-PNN neural network algorithm framing the heart sound signal using the window function, using the double threshold method to determine the value of the data, using the LMS algorithm to debug the corresponding parameters, and saving the denoised data in mat format, extracting The short-time autocorrelation coefficients and short-term power spectral densities of each heart sound are used, and PNN neural network is used to extract 40,000 sample data for training, and each heart sound is graded and predicted. After inputting the training data from the mode layer of the PNN neural network, it can be obtained through simulation test that the prediction accuracy of the LMS-PNN neural network can reach more than 96%.