Multiview clustering has attracted much attention due to its ability to aggregate various source information and many advanced approaches have been proposed in the literature. However, there are still two major issues that need to be further explored: i) how to efficiently handle large-scale data; ii) how to effectively incorporate the complementary multiple sources. In this article, we fulfill a unified multiview subspace clustering model termed anchor-based multiview subspace clustering with diversity regularization by seamlessly optimizing subspace learning and multiview fusion. First, we efficiently evaluate the self-expression similarity matrix based on sampling anchor points to reduce the high time complexities in former methods. A regularization term is further imposed to encourage high independence and diversity of each view. In addition, we theoretically analyze the time complexity of the proposed algorithm. Comprehensive experiments on several benchmark datasets demonstrate that our proposed model consistently outperforms over the state-of-the-art techniques.