Multi-view　learning　is　an　emerging　field　of　multi-modal　fusion,　which　involves　representing　a　single　instance　using　multiple　heterogeneous　features　to　improve　compatibility　prediction.　However,　existing　graph-based　multi-view　learning　approaches　are　implemented　on　homogeneous　assumptions　and　pairwise　relationships,　which　may　not　adequately　capture　the　complex　interactions　among　real-world　instances.　In　this　paper,　we　design　a　compressed　hypergraph　neural　network　from　the　perspective　of　multi-view　heterogeneous　graph　learning.　This　approach　effectively　captures　rich　multi-view　heterogeneous　semantic　information,　incorporating　a　hypergraph　structure　that　simultaneously　enables　the　exploration　of　higher-order　correlations　between　samples　in　multi-view　scenarios.　Specifically,　we　introduce　efficient　hypergraph　convolutional　networks　based　on　an　explainable　regularizer-centered　optimization　framework.　Additionally,　a　low-rank　approximation　is　adopted　as　hypergraphs　to　reformat　the　initial　complex　multi-view　heterogeneous　graph.　Extensive　experiments　compared　with　several　advanced　node　classification　methods　and　multi-view　classification　methods　have　demonstrated　the　feasibility　and　effectiveness　of　the　proposed　method.