To address issues of sparse interaction data and unreasonable learning resource planning in learning path recommendation， a Learning Path Recommendation Model integrating Multi-behavior Modeling and Reinforcement Learning （RL）， LPRMMMRL， was proposed. First， user behavior data were classified and fused into a course knowledge graph as entity nodes， and a Graph Convolutional Network （GCN） with attention mechanism was used to capture multi-source heterogeneous features. Second， three data augmentation strategies were designed from both behavioral and learning resource perspectives， and contrastive learning was applied to carry out representation learning to the augmented data， which were integrated into RL as supplementary information. Finally， after fully considering individual differences of the users， a personalized learning path recommendation model was built using a Deep Q-Network （DQN）， and a dual reward function of both knowledge points and paths was designed to guide model convergence. Experiments were conducted for comparing LPRMMMRL with learning path recommendation models such as LPG （Learning Path Generator）， KTKDM （Knowledge Tracing based Knowledge Demand Model） on four real-world courses from MOOPer and MOOCCubeX datasets. The results show that LPRMMMRL achieves at least 5.53% and 5.74% improvements in precision， recall， and F1-score on the MOOPer dataset， respectively， and at least 4.56% and 3.41% improvements on the MOOCCubeX dataset， respectively. Besides， parameter sensitivity analysis and ablation studies validate the effectiveness of the proposed model.