The purpose of subspace clustering is to segment data from different subspaces into the corresponding low-dimensional subspaces which the data essentially belong to. The existing methods based on data self-representation and spectral clustering divide this problem into two consecutive stages： first， the affinity matrix of the data was learned from the high-dimensional data， and then the cluster membership of the data was inferred by applying spectral clustering to the learned affinity matrix. A new data adaptive sparse regularization term was defined and combined with Structural Sparse Subspace Clustering （SSSC） model and improved Sparse Spectral Clustering （SSpeC） model， and a new unified optimization model was proposed. In the new model， by using the mutual guidance of data similarity and clustering indicators， the blindness of SSpeC sparsity penalty was overcome and the similarity was made to be discriminative， which was conducive to dividing the data from different subspaces into different classes， and the defect that the SSSC model only forces the data from the same subspace to have the same labels was made up. Experimental results on common datasets show that the proposed model enhances the ability of clustering discrimination and is superior to some classical two-stage methods and SSSC model.