Abstract: With the advancement of science and technology, the demand for space exploration has become particularly urgent. However, the microgravity environment in space has negative impacts on the physiological and psychological health of astronauts, including decreased bone density, muscle atrophy, and changes in cardiovascular function. These challenges pose significant barriers to the realization of long-term space habitation and deep space exploration. To address these challenges, this study integrates Microgravity Biomedical Knowledge Graphs (MBKG) and Drug Repurposing Knowledge Graphs (DRKG) to construct a comprehensive knowledge graph that covers a wide range of diseases, drugs, and genes, as well as the complex relationships between entities. Based on this, the study trains and uses a new ternary relationship prediction model, Heterogeneous Causal Meta path Graph Neural Network (HCMGNN), to obtain prediction results. The results show that compared with traditional binary link prediction in knowledge graphs, the ternary prediction method proposed in this study has a significant advantage in improving the accuracy of gene and drug predictions. The study concludes that the ternary relationship model is effective and has the potential to explore the prediction of gene-drug-disease ternary relationships, provide new methods and research ideas for the physiological and psychological health of astronauts in future space exploration and drug repurposing research, and opening up new perspectives in the field of drug repurposing.