Many of the existing multi-view clustering algorithms lack the ability to estimate the reliability of different views and thus weight the views accordingly， and some multi-view clustering algorithms with view-weighting ability generally rely on the iterative optimization of specific objective function， whose real-world applications may be significantly influenced by the practicality of the objective function and the rationality of tuning some sensitive hyperparameters. To address these problems， a Multi-view Ensemble Clustering algorithm based on View-wise Mutual Information Weighting （MEC-VMIW） was proposed， whose overall process consists of two phases： the view-wise mutual weighting phase and the multi-view ensemble clustering phase. In the view-wise mutual weighting phase， multiple random down-samplings were performed to the dataset， so as to reduce the problem size in the evaluating and weighting process. After that， a set of down-sampled clusterings of multiple views was constructed. And， based on multiple runs of mutual evaluation among the clustering results of different views， the view-wise reliability was estimated and used for view weighting. In the multi-view ensemble clustering phase， the ensemble of base clusterings was constructed for each view， and multiple base clustering sets were weighted to model a bipartite graph structure. By performing efficient bipartite graph partitioning， the final multi-view clustering results were obtained. Experiments on several multi-view datasets confirm the robust clustering performance of the proposed multi-view ensemble clustering algorithm.