Drug combination recommendation aims to provide personalized medication prescriptions for patients according to their historical visit records and current condition. Several recent studies try to utilize graph structures to better model the relationships among diagnoses, procedures, and drugs for drug combination recommendation. However, they primarily focus on pairwise relationships and fail to capture the higher-order connections and combination effects among different medical entities. Additionally, existing approaches often overlook the multifaceted nature of diagnoses and procedures within patients’ medical records. To address these issues, we propose a hierarchical hypergraph convolution network for drug combination recommendation. Specifically, we design an innovative hierarchical hypergraph structure and corresponding convolution network to capture the complex, higher-order relationships among medical entities. Furthermore, we design a capsule-based representation learning method to model the multifaceted nature of patients’ medical records for drug combination recommendation. Extensive experiments on several real-world datasets of different hospital departments demonstrate the effectiveness of the proposed approach compared with state-of-the-art methods.