In　the　realm　of　intelligent　transportation　systems,　accurately　predicting　vehicle　trajectories　is　paramount　for　enhancing　road　safety　and　optimizing　traffic　flow　management.　Addressing　the　impacts　of　complex　traffic　environments　and　efficiently　modeling　the　diverse　behaviors　of　vehicles　are　the　key　challenges　at　present.　To　achieve　precise　prediction　of　vehicle　trajectories,　it　is　essential　to　fully　consider　the　dynamic　changes　in　traffic　conditions　and　the　long-term　dependencies　of　time-series　data.　In　response　to　these　challenges,　we　propose　the　Memory-Enhanced　Spatio-Temporal　Graph　Network　(MESTGN),　an　innovative　model　that　integrates　a　Spatio-Temporal　Graph　Convolutional　Network　(STGCN)　with　an　attention-enhanced　Long　Short-Term　Memory　(LSTM)-based　sequence　to　sequence　(Seq2Seq)　encoder-decoder　structure.　MESTGN　utilizes　STGCN　to　capture　the　complex　spatial　dependencies　between　vehicles　and　reflects　the　interactions　within　the　traffic　network　through　road　traffic　data　and　network　topology,　which　significantly　influences　trajectory　prediction.　Additionally,　the　model　focuses　on　historical　vehicle　trajectory　data　points　using　an　attention-weighted　mechanism　under　a　traditional　LSTM　prediction　architecture,　calculating　the　importance　of　critical　trajectory　points.　Finally,　our　experiments　conducted　on　the　urban　traffic　dataset　ApolloSpace　validate　the　effectiveness　of　our　proposed　model.　We　demonstrate　that　MESTGN　shows　a　significant　performance　improvement　in　vehicle　trajectory　prediction　compared　with　existing　mainstream　models,　thereby　confirming　its　increased　prediction　accuracy.