Graph　structure　is　widely　used　in　the　field　of　multi-view　learning.　Hypergraph　which　is　a　kind　of　extension　of　graph　can　capture　the　higher-order　relationships　of　nodes　in　a　better　way.　However,　most　existing　hypergraphbased　models　are　based　on　the　assumption　that　hypergraph　structures　are　readily　available,　which　is　untenable　in　most　cases.　In　order　to　alleviate　this　problem,　we　propose　the　learnable　unified　hypergraph　dynamic　system　framework,　a　novel　approach　in　unified　cross-view　hypergraph　structure　generation　tailored　for　multi-view　semi-supervised　classification.　Specifically,　we　introduce　four　strategies　for　unified　cross-view　hypergraph　generation　and　propose　a　mechanism　for　generating　learnable　unified　cross-view　hypergraph.　Furthermore,　we　utilize　a　dynamic　diffusion　model　to　dynamically　learn　unified　hypergraph　structure　which　can　achieve　better　performance　in　multi-view　semi-supervised　classification　tasks.　Extensive　experimental　results　on　various　real　datasets　show　that　the　proposed　method　outperforms　other　state-of-the-art　multi-view　algorithms.