The knowledge tracing task aims to accurately track students’ knowledge status in real time and predict students’ future performance by analyzing their historical learning data. This study proposes a deep memory network knowledge tracing model incorporating knowledge point-relationships (HRGKT) to address the problem that current research has neglected complex higher-order relationships in the knowledge points covered by the questions. Firstly, HRGKT uses the knowledge point relationship graph to define the relationship information between nodes in the graph, which represents the rich information between knowledge points. GAT is used to obtain higher-order relationships between them. Then, forgetting exists in the learning process, and HRGKT considers four factors affecting knowledge forgetting to track students’ knowledge status more accurately. Finally, based on the experimental comparison results on real online education datasets, HRGKT performs more accurately in tracing students’ knowledge mastery status and has better prediction performance than current knowledge tracing models.