Aiming at the problems of high dimension of hyperspectral image and low classification accuracy caused by non-linear classification between objects, a space spectrum classification algorithm based on multi-label shared subspace and kernel ridge regression is proposed.The inseparable features of similar pixels in linear space are mapped to high-dimensional space using kernel ridge regression, which realizes the effective separation of classification characteristics in high-dimensional space,so as to improve the accuracy of the similarity of features.At the same time,the high-dimensional sample data is mapped into the low-dimensional shared subspace.In the low- dimensional environment,the multi-class label is used as a guide,and the low-rank matrix is introduced to establish the prediction relationship between the category label and the shared space, and the common characteristics among the multiple labels are mined. Improve the use of common attributes among multiple categories to improve the classification accuracy of hyperspectral images.Finally,the singular value decomposition iteration method is used to solve the objective function,which can speed up the parameter solution to a certain extent.Simulation experiments are carried out on two sets of hyperspectral datasets, Indian pines and Pavia University,compared with other similar algorithms,the overall classification accuracy,average classification accuracy and kappa coefficient of this algorithm are improved by at least 4.76%,4.24% and 5.19% at low sample ratios.Compared with the non kernel algorithm,the overall classification accuracy,average classification accuracy and kappa coefficient of the algorithm are improved by at least 2.92%,2.8% and 3.48% without increasing the running time.