KnowAcu: A Knowledge-Guided Heterogeneous Multi-Graph Learning Framework for Acupoint Prescription

As an indispensable component of Traditional Chinese Medicine (TCM), acupuncture therapy plays a pivotal role in both clinical treatment and preventive healthcare. However, generating appropriate acupoint prescriptions—structured treatment schemes tailored to complex symptoms remains a formidable challenge in automated medical reasoning. Existing studies struggle to bridge the semantic knowledge gap between diverse symptom manifestations and rigorous TCM laws, often failing to capture the inherent structural heterogeneity of symptom-acupoint interactions. In this paper, we propose KnowAcu, a Knowledge-Guided framework for precise Acupoint prescription that synergizes TCM-driven heterogeneous multi-graph structures with the semantic expertise of Large Language Models (LLMs). Specifically, we construct five distinct graphs to explicitly model the multi-faceted relational dependencies defined by TCM principles and historical prescriptions. These structures are integrated through a Multi-Graph Joint Learning (MGJL) module, which extracts multi-granular representations of symptoms and acupoints. To align the model with professional clinical logic, we leverage LLMs for auxiliary supervision and knowledge distillation, incorporate the functional hierarchies of acupoints and employ a acupoint-symptom reconstruction task for semantic consistency verification. Extensive experiments on a curated real-world clinical dataset and a large-scale simulated benchmark demonstrate that KnowAcu significantly outperforms state-of-the-art methods in both clinical applicability and model robustness.