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Polysomnography is the diagnostic gold standard for obstructive sleep apnea-hypopnea syndrome (OSAHS), requiring medical professionals to analyze apnea-hypopnea events from multidimensional data throughout the sleep cycle. This complex process is susceptible to variability based on the clinician’s experience, leading to potential inaccuracies. Existing automatic diagnosis methods often overlook multimodal physiological signals and medical prior knowledge, leading to limited diagnostic capabilities. This study presents a novel heterogeneous graph convolutional fusion network (HeteroGCFNet) leveraging multimodal physiological signals and domain knowledge for automated OSAHS diagnosis. This framework constructs two types of graph representations: physical space graphs, which map the spatial layout of sensors on the human body, and process knowledge graphs which detail the physiological relationships among breathing patterns, oxygen saturation, and vital signals. The framework leverages heterogeneous graph convolutional neural networks to extract both localized and global features from these graphs. Additionally, a multi-head fusion module combines these features into a unified representation for effective classification, enhancing focus on relevant signal characteristics and cross-modal interactions. This study evaluated the proposed framework on a large-scale OSAHS dataset, combined from publicly available sources and data provided by a collaborative university hospital. It demonstrated superior diagnostic performance compared to conventional machine learning models and existing deep learning approaches, effectively integrating domain knowledge with data-driven learning to produce explainable representations and robust generalization capabilities, which can potentially be utilized for clinical use. Code is available at