The goal of multi-view clustering is to divide data exploiting the consistent and complementary information from various views. However, the ability to represent data varies from view to view, and some views may even contain a lot of redundant and noise information which not only cannot bring diverse information, but also affect the clustering performance. In this study, an adaptive weighted low-rank constrained multi-view subspace clustering algorithm is proposed, which construct the latent consensus low-rank matrix shared by each view and each view is given adaptively learned weights. An effective iterative optimization algorithm is proposed to optimize the model. Experimental results on five real data sets show the effectiveness of the proposed algorithm.