Recommender systems are pivotal in enhancing user experience on a variety of platforms by providing personalized suggestions based on user behaviors, historical interactions, and item attributes. Recently, recommendation methods combining graph neural networks with contrastive learning have gained attention for their superior performance. However, despite their success, they still face challenges in capturing structural information in the user-item interaction graph and are sensitive to noisy user behavior and the quality of contrast views. To address these limitations, this paper proposes a novel multi-view hypergraph contrastive learning method for item recommendation (VGCL). Specifically, the method involves constructing user-item interaction view, user-user collaborative view, and item-item collaborative view by leveraging user information, item information, and user-item interactions. It then learns user and item feature vectors by incorporating semantic and higher-order correlation information through graph and hypergraph neural network techniques. The method performs contrastive learning on the user and item feature vectors to enhance their quality. Finally, it predicts user preference scores for items using multi-layer perceptron and recommends the most suitable items to users. Extensive experiments on various real datasets show that VGCL outperforms existing state-of-the-art methods.