To address knowledge-aware recommendation limitations， including insufficient high-order semantic modeling， weak exploitation of item deep correlations， and weak exploitation of knowledge graph structure feature， a knowledge-aware recommendation performance enhancement method based on Multi-heterogeneous Topology collaboration and Hierarchical Contrastive Learning （MTHCL） was proposed. First， three heterogeneous graphs were constructed： a Collaborative Filtering-based Item-item Graph （CFIG） was designed to capture collaborative filtering signals， a Knowledge Graph-based Item-entity Graph （KGIG） was established to mine semantic associations， and a User Preference Graph （UPG） was developed to model multi-hop interest evolution via dynamic attention mechanism. Then， a hierarchical contrastive learning strategy was designed to establish a complementary mechanism across heterogeneous topological views. In the local layer， semantic accuracy was ensured by focusing on the direct interaction features between users and items； in the dynamic layer， scene adaptability was enhanced by using attention weighted aggregation of multi-hop relationships； in the global layer， semantic consistency was guaranteed through fusing multi-view information. Finally， regularization term was introduced to suppress overfitting， and an integrated loss function was used to optimize the method. Experimental results demonstrate that MTHCL achieves the highest Area Under Curve （AUC） and F1-score （F1）， outperforming the best baseline method — Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System （MCCLK） by 2.23 and 2.19 percentage points on the Book-Crossing dataset， 1.24 and 1.78 percentage points on the MovieLens-1M dataset， and 1.86 and 1.97 percentage points on the Last.FM dataset. Ablation study results confirm the critical contributions of multi-view collaboration and hierarchical contrastive learning to performance enhancement， and also verify the superiority of multi-view collaboration and hierarchical contrastive learning in effectively enhancing recommendation performance.