Hypergraph representation learning helps to uncover high-order information that cannot be expressed by general graphs, which plays an important role in various fields such as bioinformatics, drug design, and recommendation systems. The combination of data augmentation and contrastive learning methods can significantly enhance the effectiveness of representation learning by increasing data diversity and enhancing the discriminative power of feature representations. However, existing hypergraph learning methods that utilize data augmentation and contrastive learning are limited, primarily focusing on bi-view contrastive learning and non-adaptive or semi-adaptive representation learning. Therefore, we propose MV-HGACL, a fully adaptive contrastive learning method for hypergraphs with a larger search space. Firstly, this paper implements fully adaptive representation learning on hypergraphs, introduces a novel multi-view contrastive learning framework, and demonstrates its effectiveness through experiments. Secondly, to optimize performance, a combination strategy for updating the generator parameters is proposed. Finally, extensive comparison and ablation experiments are conducted on four datasets, and the experimental results show the effectiveness of the proposed MV-HGACL method.