The IGBT health evaluation of power semiconductor devices is usually based on the threshold evaluation method, which is usually a single characteristic parameter evaluation system. This kind of evaluation method cannot reflect the internal correlation of the change of multiple characteristic parameters in the deep level. Multi-label classification plays an important role in machine learning and can truly reflect the internal correlation principle of multi-feature parameters. Many studies have proved that multilabel classification (mlc) can effectively increase the actual classification effect of the clustering algorithm. In this paper, a clustering algorithm based on multi-label learning is applied to the health evaluation of IGBT. There are many characteristic parameters that affect each other in the actual work of IGBT, so it is difficult for a single label to reflect its actual health status. At the same time, multi-label data often belong to multiple classifications. Multi-label learning can improve the feature dependence ability of clustering method and improve the accuracy of classification. In this paper, we propose a multi-label classification learning model based on ISODATA for the multi-feature parameters of power semiconductor device IGBT, which can comprehensively consider the multi-level correlation effect of internal parameters in the multifeature parameter extraction. The experiment results show that the algorithm model can better adapt to the IGBT health classification evaluation compared with the general clustering algorithm. INDEX TERMS IGBT health evaluation, ISODATA, multi-label classification (mlc).