The key of pattern recognition is feature extraction. Fusion of feature is an important complement of feature extraction, and it has been proved to be important to improve discrimination. Here, the sparse representation method is studied by introducing sparse representation into a high dimensional feature space and utilizing kernel trick to make sparse representation in the space.The kernel sparse representation coefficients with kernel sparse representation are utilized, then kernel sparsity preserve projection (KSPP) subspace. Moreover KSPP is brought into canonical correlation analysis (CCA), then kernel sparsity preserve canonical correlation analysis (KSPCCA) is studied. The proposed algorithm is reliable and validated on the multiple feature database and face database.