Vehicle　trajectory　prediction　is　a　challenging　problem　in　the　field　of　autonomous　driving,　witch　is　of　great　significance　to　the　safety　of　autonomous　driving　and　traffic　roads.　In　view　of　the　interaction　between　surrounding　vehicles　and　target　vehicle　and　its　own　trajectory,　we　propose　a　new　graph　network　model　to　predict　future　vehicle　trajectory.　First,　the　correlation　network　of　vehicles　at　each　time　is　constructed　based　on　the　complex　network　method.　In　order　to　make　up　for　the　lack　of　real　spatial　relevance　caused　by　the　fixed　graph,　we　propose　an　adaptive　parameter　matrix　to　coordinate　and　optimize　the　global　spatio-temporal　graph.　Second,　the　global　spatio-temporal　features　of　vehicle　historical　trajectory　data　are　extracted　by　stacked　graph　convolution　module.　Finally,　the　obtained　graph　features　are　coded　based　on　seq2seq　network,　and　the　trajectory　prediction　of　road　vehicles　at　different　times　in　the　future　is　realized.　Our　model　has　been　trained　and　verified　on　the　published　NGSIM　US-101　and　I-80　data　sets.　Compared　with　other　advanced　schemes,　our　model　has　more　accurate　results　in　the　future　time　of　5　seconds.　In　predicting　the　future　group　trajectory　of　vehicles　on　the　road,　the　accuracy　of　long-term　prediction　is　11%　higher　than　that　of　the　most　advanced　scheme.　IEEE