Academic　performance　prediction　is　a　fundamental　and　hot　issue　in　educational　data　mining　(EDM).　Recently,　researchers　have　proposed　a　series　of　effective　machine　learning　(ML)　based　classification　strategies　to　predict　students＇　academic　performance.　However,　prior　arts　are　typically　concerned　about　individual　models　but　neglect　the　association　among　students,　which　might　considerably　have　an　effect　on　the　integrity　of　the　academic　performance-related　representations.　Meanwhile,　students＇　multi-viewing　behavior　contains　complex　relations　among　students.　Therefore,　we　propose　a　Multi-View　Hypergraph　Neural　Network　(MVHGNN)　for　predicting　students＇　academic　performance.　MVHGNN　uses　hypergraphs　to　construct　high-order　relations　among　students.　The　semantic　information　implied　by　multiple　behaviors　is　consolidated　through　meta-paths.　Further,　a　Cascade　Attention　Transformer　(CAT)　module　is　introduced　to　mine　the　weight　of　different　behaviors　by　the　self-attention　mechanism.　Our　method　is　evaluated　on　real　campus　student　behavioral　datasets.　The　experimental　results　demonstrate　that　our　method　outperforms　the　state-of-the-art　ones.