Multi-view　semi-supervised　classification　primarily　aims　to　enhance　classification　accuracy　when　dealing　with　limited　labeled　samples.　Although　existing　methods　have　shown　impressive　performance,　significant　challenges　still　persist　in　efficiently　propagating　label　information　and　capturing　global　relationship　information　within　multi-view　data.　To　address　the　aforementioned　challenges,　a　Graph　Convolutional　Network　(GCN)-based　framework　is　presented,　which　explores　the　acquisition　of　multilevel　representations,　encompassing　feature-level　representations　and　the　fusion　of　global　structural　associations.　The　state-of-the-art　performance　of　the　proposed　model　is　unequivocally　supported　by　a　comprehensive　set　of　empirical　results.　©　2023　IEEE.