Abstract: Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However, current methods mainly seek the consensus embedding feature for clustering by exploring global correlations between anchor graphs or projection matrices.In this paper, we propose a simple yet efficient scalable multi-view tensor clustering (S$^2$MVTC) approach, where our focus is on learning higher-order correlations of embedding features across views. Specifically, r by stacking the embedding features of different views into a tensor and then rotating, we build a novel tensor low-frequency approximation (TLFA) operator to efficiently explore higher-order correlations. Furthermore, to enhance clustering accuracy, consensus constraints are applied to embedding features to ensure inter-view semantic consistency. Experimental results on six large-scale multi-view datasets demonstrate that S$^2$MVTC significantly outperforms state-of-the-art algorithms in terms of clustering performance and CPU execution time, especially when handling massive data.