Recommender systems face the challenge of processing massive amounts of interaction data. The underutilization of relationship information in these data, as well as the presence of noise, reduces the accuracy of recommendations and the generalization ability of recommendation models. To address this, we propose KGDRec—a multi-graph knowledge-enhanced denoising recommendation algorithm. First, a relation-aware multi-graph fusion strategy integrates user-item interactions, user-user trust relationships, and item-item relationships to enhance semantic representations and alleviate data sparsity. However, while multi-graph fusion enriches relational information, it also introduces noise. To tackle this, we design a denoising self-enhancement module that combines edge perturbation, stability weighting, and contrastive learning to suppress unreliable signals and improve representation consistency. Experiments on the Ciao, Epinions, and Yelp datasets demonstrate that KGDRec outperforms baseline models in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG) metrics, and enhances accuracy and generalization capability.
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