心血管疾病一直是威胁人类生命健康的重大因素，如果能将人类心音信号中蕴含的病理信息精准分类，则对疾病的诊断和控制会有很大的帮助.首先，采用粒子群优化算法对传统的支持向量机算法进行优化，提出1个二分类器模型，初级分类器是由基于Stacking方法融合3个算法Adaboost、RF和PSOA-SVM构成的分类器，次级分类器为LR模型；其次，利用改进后的灰狼优化算法寻找SVM最优参数组合得到新分类器模型；最后，利用心音数据集对两个分类器模型进行实验分析，通过实验证明这2种模型都表现出优秀的分类效果.

Cardiovascular disease has always been a major factor threatening human life and health. If the pathological information contained in human heart sound signals can be accurately classified， it will be very helpful for disease diagnosis and control. Firstly， particle swarm optimization algorithm is used to optimize the traditional support vector machine algorithm， and a binary classifier model is proposed. The primary classifier is composed of three algorithms Adaboost， RF and PSOA－SVM based on Stacking method， and the secondary classifier is LR model； Secondly， the improved Grey Wolf Optimization Algorithm is used to find the optimal parameter combination of support vector machine to get a new classifier model； Finally， the heart sound data set is used to analyze the two classifier models. The experiments show that the two models show excellent classification results.