Sparse representation and cooperative learning are two representative technologies in the field of multi-view spectral clustering. The former can effectively extract features of multiple views by the removal of redundant information contained in each view. The latter can incorporate the diversity of each view. However, traditional sparse representation and cooperative learning algorithms are inadequate in preserving the internal geometric features of data by manifold regularization. In fact, general approaches rarely consider the similarities between the internal graph structures of individual views. Moreover, to achieve the optimal global feature learning, we present a novel two-step multi-view spectral clustering strategy, which combines the proposed sparse representation by adaptive graph learning with adaptive weighted cooperative learning. In the first step, the proposed matrix factorization by manifold regularization can strengthen the sparse features clustering discrimination of samples of each view. Specifically, the synchronization optimization method by introducing adaptive graph learning can better retain its internal complete structure of each view. This ensures the structure correlation of views through the usage of the sparse matrix and the optimal graph similarity matrix. In the second step, the adaptive weighted cooperative learning is performed on each view to get a global optimized matrix. In order to ensure that the global matrix is associated with various view features, graph learning is also performed on the global matrix. Experiment results on several multi-view datasets and single-view datasets show that the proposed method significantly outperformed the state-of-the-art algorithms.