Abstract: Online mental health forums have become an important carrier of mental health services. Detecting psychological distress from a vast number of posts is the basis for psychological intervention. Fully utilizing the social relationships of seekers is conducive to judging their mental health status. However, most existing methods rely on explicit social relationships. They fail to pay attention to the psychological support relationships between patients and doctors (seekers and supporters). These relationships are based on the patient’s personal experiences, symptom causes, self-cognition and psychological support expertise. Thus, this paper takes suicidal ideation as the detection target and proposes Post-User Psychological Support Heterogeneous Graph (PU-PSHG). PU-PSHG is used to represent the semantics of posts and the doctor-patient relationships between seekers and supporters in online mental health forums. According to PU-PSHG, Graph-enhanced Suicide Ideation Detection (GSID) model is proposed. Firstly, based on the definition of psychological support relationships, the semantics of two meta-paths (user-to-user and user-to-post) are defined, and PU-PSHG containing users and posts are constructed. DeepWalk algorithm is used to learn doctor-patient relationships or community relationships from PU-PSHG. Then, the representation of psychological support relationships is learned through relational features. Besides the post semantics and doctor-patient relationships are fused based on heterogeneous relationships. Finally, suicide ideation detection is performed based on the representation of posts. Experimental results on CLPsych2017 shaRed task show that GSID has better performance compaRed with existing methods. CompaRed to C-GraphSAGE, GSID improved by 7.8%, 4.8%,1.4% in Non-green F1, All F1, All Acc, respectively. Ablation experiments found that removing three different types of relationships (the reply relationship between posts, the psychological support relationship between users and posts, the psychological support relationship between users and users) from PU-PSAG led to decreases in Non-green F1 of 3.04%, 3.80%, 6.17% respectively.