In　object-stacking　scenes,　robotic　manipulation　is　one　of　the　most　important　research　topics　in　robotics.　It　is　particularly　significant　to　reason　object　relationships　and　obtain　intelligent　manipulation　order　for　more　advanced　interaction　between　the　robot　and　the　environment.　However,　many　existing　methods　focus　on　individual　object　features　and　ignore　contextual　information,　leading　to　great　challenges　in　efficiently　reasoning　manipulation　relationship.　In　this　paper,　we　introduce　a　novel　graph-based　visual　manipulation　relationship　reasoning　architecture　that　directly　outputs　object　relationships　and　manipulation　order.　Our　model　first　extracts　features　and　detects　objects　from　RGB　images,　and　then　adopts　Graph　Convolutional　Network　(GCN)　to　collect　contextual　information　between　objects.　Moreover,　a　relationship　filtering　network　is　built　to　reduce　object　pairs　before　reasoning　and　improve　the　efficiency　of　relation　reasoning.　The　experiments　on　the　Visual　Manipulation　Relationship　Dataset　(VMRD)　show　that　our　model　significantly　outperforms　previous　methods　on　reasoning　object　relationships　in　object-stacking　scenes.