Sevenlevel inverter has a complex structure and several fault attributes cross each other, thus reducing the discrimination among similar fault classes. Given this, a supervised kernel shared nearest neighbor (SKSNN) algorithm was proposed and applied to locality preserving projection (LPP), which formed a new feature extraction algorithm for The IGBT opencircuit fault feature extraction of cascade sevenlevel inverter. Firstly, the threephase current of the AC side was collected as the original signal corresponding to each fault status. On that basis, the lowdimensional sensitive features embedded in raw data would be extracted by the SKSNNLPP algorithm. Then, the extracted fault features were taken as the input of support vector machine （SVM） to establish fault diagnosis model. Finally, through the comparative analysis of the diagnostic effects, it can be shown that the proposed method is superior to traditional signal processing and statistical analysis methods, which can effectively reduce the misdiagnosis rate of similar fault categories and can achieve 964% diagnostic accuracy.