Multi-view subspace clustering is a method for learning a unified representation of all views from subspaces and exploring the latent clustering structure of data. As a clustering approach for processing high-dimensional data, subspace clustering has become a focal point in the field of multi-view clustering. Multi-view low-rank sparse subspace clustering method combines low-rank representation and sparse constraints. During the construction of the affinity matrix, this algorithm utilizes low-rank sparse constraints to capture both global and local structures of the data, thereby optimizing the performance of subspace clustering. The three-way decision, rooted in the rough set model, is a decision-making concept often applied in clustering algorithms to reflect the uncertainty relationship between objects and clusters during the clustering process. In this study, inspired by the idea of the three-way decision, a voting system is designed as the decision basis. The system is integrated with multi-view sparse subspace clustering to form a unified framework, resulting in a novel algorithm. Experimental results on various artificial and real-world datasets demonstrate that this algorithm can enhance the accuracy of multi-view clustering.