During the clustering process of high?dimensional data with feature missing， there are problems of the curse of dimensionality caused by data high dimension and the invalidity of effective distance calculation between samples caused by data feature missing. To resolve above issues， a K?Nearest Neighbor （KNN） imputation subspace clustering algorithm for high?dimensional data with feature missing was proposed， namely KISC. Firstly， the nearest neighbor relationship in the subspace of the high?dimensional data with feature missing was used to perform KNN imputation on the feature missing data in the original space. Then， multiple iterations of matrix decomposition and KNN imputation were used to obtain the final reliable subspace structure of the data， and the clustering analysis was performed in that obtained subspace structure. The clustering results in the original space of six image datasets show that the KISC algorithm has better performance than the comparison algorithm which clusters directly after interpolation， indicating that the subspace structure can identify the potential clustering structure of the data more easily and effectively； the clustering results in the subspace of six high?dimensional datasets shows that the KISC algorithm outperforms the comparison algorithm in all datasets， and has the optimal clustering Accuracy and Normalized Mutual Information （NMI） on most of the datasets. The KISC algorithm can deal with high?dimensional data with feature missing more effectively and improve the clustering performance of these data.