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This is especially true for high\u2010complexity datasets from platforms like Xenium. We present SemanticST, a graph neural network (GNN) framework that addresses this challenge through a fundamentally different design. SemanticST is the first GNN method to implement mini\u2010batch training, enabling scalable analysis of massive ST datasets (validated on Xenium). Crucially, it employs a multi\u2010semantic graph fusion strategy that learns disentangled biological representations across tissue, using a min\u2010cut loss that requires neither graph corruption nor contrastive sampling. Benchmarking across diverse tissues (e.g., brain, embryo, tumor) confirms consistent superiority. It achieves up to 20% higher ARI\/NMI on the gold\u2010standard brain cortex and uniquely delineates all mouse olfactory bulb layers and hippocampal sub\u2010regions. 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