By directly processing each view of original data, multi-view subspace clustering algorithms typically obtain potential subspace representation matrices. However, these methods often underestimate the influences of redundant data, making it challenging to accurately capture the accurate clustering results in the potential subspace representation. Furthermore, the K-means algorithm used to produce the clustering results easily neglects the local structure of the data within the subspaces, leading to unstable results. To address the aforementioned problems, this study proposes a multi-view subspace method to acquire high-quality subspace representations. Specifically, the study initially gets a robust representation through a feature decomposition method. Then, it constructs a joint latent subspace representation for multiple views. Next, it uses spectral rotation to obtain clustering results and employs orthogonal constraints on the partition matrix to reconstruct the subspaces, thereby enhancing clustering performance. Finally, an iterative optimization algorithm is applied to solve relevant optimization problems. Experiments are conducted on five benchmark datasets, and the results demonstrate that the proposed algorithm is more effective than recent multi-view clustering algorithms.