Sparse principal component analysis is an unsupervised method for dimensionality reduction and feature selection. An adaptive sparse principal component analysis （ASPCA） algorithm is proposed， because the principal load vectors do not have the same sparse pattern when calculating multiple principal components， and it is difficult to determine a small number of the variables that contribute the most to the principal components from the original feature space. Firstly， the group lasso model is used， and the ASPCA formula is obtained by applying block sparse constraints on the load vector. Subsequently， different adjustment parameters are used for different columns of the sparse matrix to obtain adaptive penalty. Finally， the block-coordinate descent method is used to optimize the adaptive sparse principal component analysis formula in two stages， so as to find the sparse load matrix and the orthogonal matrix and achieve the optimization of dimensionality reduction. The comparison results of the sparse principal component analysis （SPCA） algorithm， the structured and sparse principal component analysis （SSPCA） algorithm and the ASPCA algorithm show that the ASPCA algorithm has better dimensionality reduction performance and can extract more valuable features， thereby effectively improving the average classification accuracy of the classification model.