Human　action　recognition　and　prediction　with　skeleton-based　data　has　been　widely　applied　in　intelligent　robots　and　machine　vision.　Current　advanced　methods　mainly　focus　on　using　recurrent　neural　network　(RNN)　to　predict　human　motion　based　only　on　skeleton　data.　However,　objects　that　interact　with　the　human　body　will　have　a　great　impact　on　human　behaviors.　Extracting　body　information　without　considering　environmental　objects　constraints　reduces　the　accuracy　of　behavior　prediction.　In　this　paper,　we　propose　a　scene-perception　architecture,　which　implements　an　end-to-end　human　behavior　prediction　task　based　on　both　objects　and　human　skeleton.　We　propose　a　scene-perception　Graph　Convolutional　Network　(SPGCN)　to　formula　the　natural　constraint　between　skeleton　nodes　and　objects　in　the　scene　to　predict　human　postures.　SPGCN　is　composed　of　graph　convolutional　network　with　scene-aware　and　RNN,　GCN　is　used　to　learn　the　dependence　relationships　between　human　and　objects.　Then,　the　learned　features　are　fed　into　RNN　to　predict　future　pose　and　action　labels.　We　evaluate　SPGCN　on　CAD-120　dataset.　Experiments　show　that　our　proposed　method　achieves　promising　results　compared　with　the　state-of-the-art　methods.　©　2021　IEEE.