Multi-way or tensor data analysis has attracted increasing attention recently, with many important applications in practice. In recent years, scholars have proposed many multi-view clustering methods based on tensor low rank representation. However, most of them use t-svd based kernel norms that have a strong dependence on low rank, making it difficult to flexibly handle image restoration problems in different scenarios and achieve good clustering results. Inspired by the ideas used in the enhanced tensor robust principal component analysis model, this paper proposes an enhanced low rank tensor multi view clustering method. Similar to the TLRR method, this method represents the multi view clustering problem of data as a low rank tensor learning problem, which is solved through the alternating direction method of multipliers. The experimental results on three image datasets show that this method is more efficient than existing methods.