This　paper　proposes　a　novel　approach　to　semantic　representation　learning　from　multi-view　datasets,　distinct　from　most　existing　methodologies　which　typically　handle　single-view　data　individually,　maintaining　a　shared　semantic　link　across　the　multi-view　data　via　a　unified　optimization　process.　Notably,　even　recent　advancements,　such　as　Co-GCN,　continue　to　treat　each　view　as　an　independent　graph,　subsequently　aggregating　the　respective　GCN　representations　to　form　output　representations,　which　ignores　the　complex　semantic　interactions　among　heterogeneous　data.　To　address　the　issue,　we　design　a　unified　framework　to　connect　multi-view　data　with　heterogeneous　graphs.　Specifically,　our　study　envisions　multi-view　data　as　a　heterogeneous　graph　composed　of　shared　isomorphic　nodes　and　multi-type　edges,　wherein　the　same　nodes　are　shared　across　different　views,　but　each　specific　view　possesses　its　own　unique　edge　type.　This　perspective　motivates　us　to　utilize　the　heterogeneous　graph　convolutional　network　(HGCN)　to　extract　semantic　representations　from　multi-view　data　for　semi-supervised　classification　tasks.　To　the　best　of　our　knowledge,　this　is　an　early　attempt　to　transfigure　multi-view　data　into　a　heterogeneous　graph　within　the　realm　of　multi-view　semi-supervised　learning.　In　our　approach,　the　original　input　of　the　HGCN　is　composed　of　concatenated　multi-view　matrices,　and　its　convolutional　operator　(the　graph　Laplacian　matrix)　is　adaptively　learned　from　multi-type　edges　in　a　data-　driven　fashion.　After　rigorous　experimentation　on　eight　public　datasets,　our　proposed　method,　hereafter　referred　to　as　HGCN-MVSC,　demonstrated　encouraging　superiority　over　several　state-of-the-art　competitors　for　semi-supervised　classification　tasks.