Personalized Learning Path Recommendation (LPR) is an important task for online education systems. Multi-objective LPR (MLPR) emphasizes multiple objectives in the learning path. It considers factors like student motivation and the adaptability of learning resources, enhancing the practical relevance of the research. This paper proposes a novel MLPR method via hierarchical reinforcement learning and knowledge tracing. It adopts a three-layer reinforcement learning framework (TRF), employing a knowledge tracing model (KTM) to assess the suitability of candidate learning paths. In TRF, the high-level agent plans learning goals; the mid-level agent considers multiple objectives and selects learning paths; the low-level agent recommends relevant learning items; the knowledge concept graph limits agents’ action space. Experiments conducted on real-world datasets validated the effectiveness of our model in improving student learning status and multi-adaptive metrics compared to other baselines.