Existing　video　salient　object　detection　(VSOD)　methods　focus　on　exploring　either　short-term　or　long-term　temporal　information.　However,　temporal　information　is　exploited　in　a　global　frame-level　or　regular　grid　structure,　neglecting　inter-frame　structural　dependencies.　In　this　article,　we　propose　to　learn　long-term　structural　dependencies　with　a　structure-evolving　graph　convolutional　network　(GCN).　Particularly,　we　construct　a　graph　for　the　entire　video　using　a　fast　supervoxel　segmentation　method,　in　which　each　node　is　connected　according　to　spatio-temporal　structural　similarity.　We　infer　the　inter-frame　structural　dependencies　of　salient　object　using　convolutional　operations　on　the　graph.　To　prune　redundant　connections　in　the　graph　and　better　adapt　to　the　moving　salient　object,　we　present　an　adaptive　graph　pooling　to　evolve　the　structure　of　the　graph　by　dynamically　merging　similar　nodes,　learning　better　hierarchical　representations　of　the　graph.　Experiments　on　six　public　datasets　show　that　our　method　outperforms　all　other　state-of-the-art　methods.　Furthermore,　We　also　demonstrate　that　our　proposed　adaptive　graph　pooling　can　effectively　improve　the　supervoxel　algorithm　in　the　term　of　segmentation　accuracy.