When the sparse subspace clustering algorithm processes hyperspectral remote sensing images， the classification accuracy of features is low. In order to improve the accuracy of feature classification， this paper proposes a weighted block sparse subspace clustering algorithm based on information entropy （EBSSC）. The introduction of information entropy weight and block diagonal constraint can obtain the prior probability that two pixels belong to the same category before the simulation experiment， so that the solution solved by the positive intervention model becomes the optimal approximate solution of the block diagonal structure， making the model obtain the performance against noise and outliers， thereby improving the discriminative ability of model classification to obtain better classification accuracy of ground features. Experimental results on three classical hyperspectral remote sensing data sets show that the clustering effect of hyperspectral image in this paper is better than that of several existing classical and popular subspace clustering algorithms.