Copy　number　variation　(CNV)　refers　to　the　number　of　copies　of　a　specific　sequence　in　a　genome　and　is　a　type　of　chromatin　structural　variation.　The　development　of　the　Hi-C　technique　has　empowered　research　on　the　spatial　structure　of　chromatins　by　capturing　interactions　between　DNA　fragments.　We　utilized　machine-learning　methods　including　the　linear　transformation　model　and　graph　convolutional　network　(GCN)　to　detect　CNV　events　from　Hi-C　data　and　reveal　how　CNV　is　related　to　three-dimensional　interactions　between　genomic　fragments　in　terms　of　the　one-dimensional　read　count　signal　and　features　of　the　chromatin　structure.　The　experimental　results　demonstrated　a　specific　linear　relation　between　the　Hi-C　read　count　and　CNV　for　each　chromosome　that　can　be　well　qualified　by　the　linear　transformation　model.　In　addition,　the　GCN-based　model　could　accurately　extract　features　of　the　spatial　structure　from　Hi-C　data　and　infer　the　corresponding　CNV　across　different　chromosomes　in　a　cancer　cell　line.　We　performed　a　series　of　experiments　including　dimension　reduction,　transfer　learning,　and　Hi-C　data　perturbation　to　comprehensively　evaluate　the　utility　and　robustness　of　the　GCN-based　model.　This　work　can　provide　a　benchmark　for　using　machine　learning　to　infer　CNV　from　Hi-C　data　and　serves　as　a　necessary　foundation　for　deeper　understanding　of　the　relationship　between　Hi-C　data　and　CNV.