Aiming at the heart sound envelope burr produced by Hilbert transform in the hidden semi-Markov model（HSMM）based on logistic regression， an HSMM combining support vector machine （SVM） and Shannon energy is proposed. First， the wavelet denoising method is used to denoise the heart sound， the heart sound is labeled according to the R peak and T wave， and the Shannon energy envelope and other characteristics are extracted. Then， the HSMM related parameters are trained based on the logistic regression model （LR）， and the most possible state is deduced with the help of Viterbi algorithm. Finally， the first heart sound S₁ and the second heart sound S₂ are identified through the SVM model. The algorithm does not need to set a hard threshold， effectively suppresses noise， and is more helpful for envelope extraction. Experimental results show that the segmentation accuracy of the proposed algorithm is significantly improved compared with the reference algorithm， with good anti-noise performance and better segmentation results.