Abstract: Personalized learning resource recommendation is derived from identifying learners' interests and recommending interesting and relevant learning resources accordingly. However, learners’ interests are influenced by various factors such as knowledge points, learning resources, and courses, which makes it a challenging task to accurately represent their interests. Additionally, these interests evolve dynamically over time, complicating the task of identifying learning interest patterns. To address this challenge, we propose a learning resource recommendation method based on spatio-temporal multi-granularity interest modeling, which is characterized as follow: An innovative architecture is designed and implemented for learning interest representation that integrates the learning space and temporal dimension in a heterogeneous graph-based learning space and the multi-granularity interest representation. The nodes in this graph represent entities, such as knowledge points, learning resources, courses, teachers, and schools; and the edges of the graph represent the inter-entity relationships. A graph neural network is utilized to express the multi-granularity interest in these nodes. Moreover, we propose a temporal multi-granularity interest pattern representation method by combining multi-dimensionality of time, learning space, and course preference, and slicing through the sequence of learner's historical behaviors is used to mine the learner's different granularity of interest patterns in the near-term within-course, mid-term across-course, and long-term across-course. Then, a multi-granularity interest adaptive fusion layer is proposed to fuse multi-granularity interest representations and interest patterns. Based on this method a multi-granularity interest self-supervision task is designed to solve the problem of lack of supervised signaling for spatio-temporal multi-granularity interests, and recommend relevant learning resources for learners via prediction layer. Our experimental results show that on MOOCCube dataset the proposed method outperforms the optimal comparison algorithms HinCRec in Recall@20 and NDCG@20 metrics by 3.13% and 7.45%, respectively. On MOOPer dataset, the proposed method outperforms optimal comparison algorithm HinCRec in Recall@20 and NDCG@20 metrics by 4.87% and 7.03%, respectively.