Multi-hypergraph convolutional neural network for herb recommendation

As the core of Traditional Chinese Medicine (TCM) diagnosis and treatment, herb recommendation aims to recommend a group of herbs according to patients’ symptoms. Existing graph-based methods mainly model pairwise relations and either omit explicit syndrome and state-element knowledge or represent the common induction of multiple patients through independent pairwise edges, thereby failing to model it as a single high-order relation. To address this limitation, we aim to improve herb recommendation by explicitly modeling the high-order patient relations induced by shared syndromes and state-element configurations. We propose a multi-hypergraph convolutional neural network (MHGCN) that represents each patient as a node and constructs a syndrome-induced patient hypergraph and a state-element-induced patient hypergraph. In each hypergraph, one hyperedge connects all patients sharing the same syndrome or exact state-element configuration. Parallel encoders learn from the two hypergraphs, and their representations are fused for multi-label herb prediction using a frequency-weighted binary cross-entropy loss. Across ten runs, MHGCN achieved mean F1-score@5 values of 70.532% on the Treatise on Febrile Diseases dataset and 29.737% on the Dictionary of Traditional Chinese Medicine Prescriptions dataset, respectively. It outperformed all evaluated topic-model, graph-based, and large language model baselines. In paired analyses using matched random seeds, its improvements over L i g h t G C N 𝑀 in Precision@5 and F1-score@10 remained significant under both paired 𝑡 -tests and Wilcoxon signed-rank tests after Holm correction ( 𝑝 a d j < 0 . 0 5 ). These findings demonstrate that representing each common induction event as a patient hyperedge enables MHGCN to model syndrome- and state-element-induced high-order relations directly and supports its effectiveness for herb recommendation.