Multi-view clustering has attracted more and more attention in the fields of image processing, data mining, and machine learning. Existing multi-view clustering algorithms have two shortcomings. One is that in the process of graph construction, only the pairwise relationship between each view data is considered to generate an affinity matrix, which lacks the characterization of neighborhood relationships; the second is that existing methods separate the process of multi-view information fusion and clustering, thereby reducing the clustering performance of the algorithm. Therefore, this study proposes a more accurate and robust joint spectral embedding multi-view clustering algorithm based on bipartite graphs. Firstly, based on the multi-view subspace clustering idea,bipartite graphs are constructed, and similar graphs are generated.Then the spectral embedding matrix of similar graphs is used to perform graph fusion. Secondly, by considering the importance of each view during the fusion process, weight constraints are applied, and an indicator matrix is introduced to obtain the final clustering result. A model is proposed to optimize the bipartite graph, embedding matrix, and clustering indicator matrix within a single framework. In addition, a fast optimization strategy for solving the model is provided, which decomposes the optimization problem into small module subproblems and efficiently solves them through iterative steps. The proposed algorithm and existing multi-view clustering algorithms have been experimentally analyzed on real data sets. Experimental results show that the proposed algorithm is more effective and robust in dealing with multi-view clustering problems compared with existing methods.