Recent research has increasingly utilized knowledge graphs (KG) to enhance recommendation system performance. However, traditional knowledge-aware recommendation models often encounter noisy data challenges in user-item interaction graphs and KG. Furthermore, these systems tend to prioritize frequently interacted popular items, neglecting less popular yet potentially relevant content, which compromises recommendation personalization and diversity. To address these issues, we propose the Knowledge Graph Denoising Dual-Contrastive Recommendation Model (KG-DCRec). Our approach performs denoising and reconstruction on low-attention edges in the KG while filtering high-attention edges. It effectively applies Singular Value Decomposition (SVD)-based denoising to the user-item interaction graph to extract users’ latent interests. In addition, we introduce a dual-contrastive learning mechanism that contrasts the denoised user-item interaction graph with the original interaction graph and the denoised knowledge graph. This design enables the model to adapt to users’ evolving preferences while enhancing its discriminative capability and generalization from multi-perspective representations. Extensive experiments on four datasets demonstrate that KG-DCRec outperforms state-of-the-art models.