Video　object　segmentation　(VOS)　exhibits　heavy　occlusions,　large　deformation,　and　severe　motion　blur.　While　many　remarkable　convolutional　neural　networks　are　devoted　to　the　VOS　task,　they　often　mis-identify　background　noise　as　the　target　or　output　coarse　object　boundaries,　due　to　the　failure　of　mining　detail　information　and　high-order　correlations　of　pixels　within　the　whole　video.　In　this　work,　we　propose　an　edge　attention　gated　graph　convolutional　network　(GCN)　for　VOS.　The　seed　point　initialization　and　graph　construction　stages　construct　a　spatio-temporal　graph　of　the　video　by　exploring　the　spatial　intra-frame　correlation　and　the　temporal　inter-frame　correlation　of　superpixels.　The　node　classification　stage　identifies　foreground　superpixels　by　using　an　edge　attention　gated　GCN　which　mines　higher-order　correlations　between　superpixels　and　propagates　features　among　different　nodes.　The　segmentation　optimization　stage　optimizes　the　classification　of　foreground　superpixels　and　reduces　segmentation　errors　by　using　a　global　appearance　model　which　captures　the　long-term　stable　feature　of　objects.　In　summary,　the　key　contribution　of　our　framework　is　twofold:　(a)　the　spatio-temporal　graph　representation　can　propagate　the　seed　points　of　the　first　frame　to　subsequent　frames　and　facilitate　our　framework　for　the　semi-supervised　VOS　task;　and　(b)　the　edge　attention　gated　GCN　can　learn　the　importance　of　each　node　with　respect　to　both　the　neighboring　nodes　and　the　whole　task　with　a　small　number　of　layers.　Experiments　on　Davis　2016　and　Davis　2017　datasets　show　that　our　framework　achieves　the　excellent　performance　with　only　small　training　samples　(45　video　sequences).　©　2023　Copyright　held　by　the　owner/author(s).　Publication　rights　licensed　to　ACM.