<p>Recent advancements in spatial transcriptomics have transformed tumor microenvironment research by providing insights into cellular interactions and spatial heterogeneity. A fundamental challenge is the precise delineation of spatial domains. However, existing methods remain limited in accurately identifying spatial domains, partially due to their reliance on single-view features. Moreover, these methods often struggle with many-to-many spot relationships, such as shared biological functions. To this end, we propose HAST, a hypergraph-driven spatial multimodal fusion tool for precise domain delineation and tumor microenvironment decoding. HAST integrates gene expression, spatial coordinates, and histological features to construct local hypergraphs that effectively model many-to-many spatial relationships. These local hypergraphs are dynamically aggregated into a global hypergraph, capturing higher-order interactions. To learn discriminative and biologically meaningful representations, we employ a hypergraph convolutional network, coupled with self-supervised contrastive learning, to fuse multi-view information. Extensive benchmarking across multiple datasets demonstrates that HAST outperforms state-of-the-art methods, accurately delineating spatial domains and uncovering domain-associated genes. Functional enrichment analyses further reveal biologically relevant pathways and provide novel insights into tumor microenvironment. In summary, HAST is a robust framework for decoding the spatial complexity of tumors, paving the way for precise spatial omics analyses in cancer research.</p>