Most existing multi-view clustering algorithms suffer from issues such as incomplete fusion mechanisms， insufficient exploration of multi-view collaborative relationships， and weak robustness. These limitations result in low consistency in clustering results and unstable performance under noise and redundant information. To address these issues， a Multi-View Clustering algorithm based on Bipartite Graph and Consensus graph learning （BGC-MVC） was developed to enhance clustering consistency and complementarity by integrating information from multiple views. Specifically， BGC-MVC constructed a bipartite graph to capture neighborhood relationships across different views， and then learned a consensus graph to strengthen inter-view similarity. It integrated embeddings of the original multi-view data into a unified framework that combined graph learning with clustering process， thereby improving the overall clustering performance. Experimental results demonstrate that BGC-MVC achieves significant improvements in accuracy， F-score， Normalized Mutual Information （NMI） and purity under convergence conditions. Notably， on the MSRC_v1 dataset， BGC-MVC outperforms Large-scale Multi-View Subspace Clustering （LMVSC） by increasing the F-score by 19.48 percentage points and exhibits enhanced robustness and accuracy.