<p>Conversational Recommender Systems (CRSs) are designed to deeply understand user intent and provide personalized recommendations and responses through natural language interactions. However, existing methods primarily face two core challenges. First, the naive fusion of multi-source heterogeneous information tends to introduce redundant and conflicting signals into user representations, limiting the fine-grained characterization of user preferences. Second, factors such as colloquial expressions and noise within knowledge graphs significantly compromise the robustness of user representations, thereby affecting the stability of both recommendation and conversation tasks. To address these issues, this paper proposes a Multi-View Hypergraph Disentanglement and diffusion denoising framework (MVHD). Specifically, the proposed method first constructs three complementary hypergraph views–collaborative attributes, structural semantic, and similar user groups–to structurally characterize user preferences from distinct perspectives. A dynamic feature disentanglement mechanism is then employed to explicitly extract common and view-specific features, followed by personalized integration to construct a well-disentangled initial user representation. Furthermore, a condition-guided diffusion denoising strategy is designed. By leveraging collaborative attribute information as guidance, this strategy explicitly purifies noise within the initial user representation through forward noise injection and reverse denoising processes, ultimately yielding a more robust and refined user representation. Extensive experiments on two benchmark datasets demonstrate that MVHD significantly outperforms existing state-of-the-art methods in both recommendation and conversation subtasks.</p>