With the growing number of services, developers struggle to efficiently discover and compose suitable ones for Mashup development, underscoring the need for intelligent service recommendation. Although recent graph- and hypergraph-based methods with contrastive learning show promise, they still face challenges such as degraded feature representations, fuzzy high-order association modeling, hyperedge sparsity, biased negative sampling, and entangled feature couplings. To address these issues, we propose a Multi-view heterogeneous Hypergraph augmented self-Gating Contrastive Fusion framework (MHGCF) for service recommendation. MHGCF constructs heterogeneous hypergraphs from interaction, semantic, and category views to capture diverse mashup–service relations. A self-gating mechanism refines neighbor aggregation to suppress noise and retain discriminative features, while a cross-view attention module enables fine-grained fusion and disentanglement. Furthermore, hypergraph perturbation creates structural views for contrastive learning, enhancing representation consistency and alleviating sparsity. Experiments on a real-world dataset show that MHGCF outperforms state-of-the-art methods, demonstrating its effectiveness and superiority.