Knowledge graph-based recommendation methods learn representations of user and item nodes by integrating relational connections from both item-attribute graph and user-interaction graph to recommend suitable items. However， since knowledge graphs contain both noisy and high-quality relations， the primary challenge lies in circumventing noisy relations while effectively mining valuable relations. Existing methods typically rely on global reconstruction strategies to optimize knowledge graph relations through single manner of pruning noisy relations or mining high-quality relations， thereby learning user and item representations. However， global perspectives struggle to sufficiently capture detailed local information and tend to overlook potential complementarity between local and global information. Furthermore， solely employing pruning or supplementation strategies fail to simultaneously mitigate interference of noisy relations and comprehensively exploit high-quality relations. To address these issues， a Recommendation method based on Multi-Perspective Relation-Enhanced Knowledge Graph （RMPREKG） was proposed. This method alleviated noise impacts in both item-attribute graphs and user?interaction graphs through an item hybrid relation alignment module and an interaction hybrid relation enhancement module， while deeply exploring valuable high-order relations. The item hybrid relation alignment module extracted local and global relations separately via importance pruning strategy and high-order relation mining approach， followed by a knowledge alignment method to synergize both types of information for effectively refining high-quality item auxiliary information. The interaction hybrid relation enhancement module constructed local hybrid pruning relation graph and global hybrid supplementation relation graph， with cross-channel and cross-layer contrastive learning employed to enhance their informational complementarity， thereby comprehensively learning user and item representations. Finally， a hierarchical gated adaptive fusion method was adopted to integrate multiple sets of user and item embeddings for recommendation. When the recommendation length is 20， compared to VRKG4Rec （Virtual Relational Knowledge Graphs for Recommendation）， RMPREKG achieves a 10.17% improvement in Normalized Discounted Cumulative Gain （NDCG） on the Last.FM dataset and a 1.13% improvement in NDCG on the MovieLens-1M dataset.