<p>With the increasing prevalence of incomplete multi-view data in practical situations, incomplete multi-view clustering (IMVC) has gained prominence as an essential methodology for processing incomplete and unlabeled multi-view data in real-world scenarios. Nevertheless, some irrelevant and insignificant data leads to sub-optimal solutions when directly modeling the raw data. And previous methods failed to fully utilize cross-view interactions, neglecting the consistency and heterogeneity among different views. Furthermore, the previous methods ignored multi-level data structures, thus limiting the exploration of hierarchical relationships. In response to the mentioned limitations, a novel method HSIMTC is proposed, which introduces a hierarchical tensor designed to harness multi-level data structures and intricate high-order correlations across multiple layers. Specifically, an orthogonal projection operator is optimized to filter out irrelevant content and extract the most critical and distinguishing features from the raw data for further analysis. In addition, dynamic cross-view cooperation facilitates information interaction and sufficiently explores consistency from different views. Meanwhile, a hierarchical tensor is first proposed which evolves bottom-up, unlike conventional flat modeling. Each layer not only consolidates internal structures from the other layers but also improves layer-to-layer interactions, enabling a deeper insight into multi-level data. Additionally, our method adopts an information simplification strategy to eliminate extraneous data and deeply analyzes the angular information of its principal direction, resulting in a more discriminative affinity matrix for spectral clustering. Experimental results on various benchmark datasets verify that HSIMTC achieves superior outcomes compared to current advanced algorithms.</p>