The principal component analysis(PCA) is one of the important methods for feature extraction，but it can’t provided more classification information by itself. In order to pick up feature information in favor of recognition from PCA eigenvector，a weight sparse principal component analysis is proposed in the paper. It achieves image de-noising function by using primitive PCA algorithm，acquires the group of weight values which are able to maximize within-class distance and minimize between-class distance in PCA feature space by utilizing Lagrange multiplier，and finishes dimension reduction by using sparse PCA(SPCA) to retain effectively some classification information of eigenvectors with little eigenvalue. In the end，the proposed algorithm is tested on an all-known public face database. The experiment results indicate the proposed algorithm has not only faster running speed but also better rate of recognition.