In recent year, with the development of various network platforms, there are always a lot of similar local community structures between users in different networks. In consideration of some single-view community detection algorithms cannot find the multi-factor community structures, in this paper we present a Multi-view Local collaborative Selecting Clustering model (called co-MLSC). This model can solve many constraints problems (like nodes, clusters, and sufficient information) and over adjustment problems. Firstly, the model can build a choice regulate matrix that can train the common part of the node set, and converge its common structure. Then we also build a local optimization matrix that regards the node structure as a training set, and uses the KRR algorithm to complete the division of isolated nodes. Finally, we use the UCI and DBLP data sets to demonstrate the effectiveness and applicability of our algorithm.