Recommendation plays a crucial role in the modern Web ecosystem, powering personalized services across e-commerce, social platforms, and online content networks. To model complex user–item interactions in such Web environments, Graph Neural Networks (GNNs) have become a popular and effective approach due to their ability to capture relational dependencies. However, existing GNN-based methods face some challenges, such as limited capacity for nonlinear representation, inability to capture global structural information, and susceptibility to noise in user interaction data. Although self-supervised learning methods have been introduced to address these issues, these methods often overlook the intricate dependencies between users and items and fail to effectively utilize high-order global information. To address these challenges, we propose Fourier Kolmogorov-Arnold Network and Hypergraph Enhanced Contrastive Learning (FHCL) for recommendation. Our method constructs two complementary views: a graph generative view and a denoising view. In the graph generative view, we use the Fourier Kolmogorov-Arnold Network (Fourier KAN) to enhance the nonlinear representation capabilities by decomposing complex user-item interactions. Subsequently, we employ Variational Graph Auto-Encoders (VGAE) to reconstruct the graph structure, extracting meaningful structural information while mitigating the impact of noise. Then we use hypergraph learning to capture high-order global dependencies. In the denoising view, we introduce a denoising matrix to filter noisy edges and further combine hypergraph learning to improve user preferences. Finally, we integrate these views through contrastive learning to generate robust and accurate recommendations. Extensive experiments on two public datasets demonstrate the superior performance of FHCL, while comprehensive ablation studies validate the necessity and effectiveness of each component.