Traffic　flow　prediction　plays　a　crucial　role　in　the　development　of　smart　cities.　However,　existing　studies　face　challenges　in　effectively　capturing　spatio-temporal　contexts,　handling　hierarchical　temporal　features,　and　understanding　spatial　heterogeneity.　To　better　manage　the　spatio-temporal　correlations　inherent　in　traffic　flow,　we　present　a　novel　model　called　Dynamic　Spatio-Temporal　Memory-Augmented　Network　(DSTMAN).　Firstly,　we　design　three　spatial-temporal　embeddings　to　capture　dynamic　spatial-temporal　contexts　and　encode　the　unique　characteristics　of　time　units　and　spatial　states.　Secondly,　these　three　spatial-temporal　components　are　integrated　to　form　a　multi-scale　spatial-temporal　block,　which　effectively　extracts　hierarchical　spatial-temporal　dependencies.　Finally,　we　introduce　a　meta-memory　node　bank　to　construct　an　adaptive　neighborhood　graph,　implicitly　representing　spatial　relationships　and　enhancing　the　learning　of　spatial　heterogeneity　through　a　secondary　memory　mechanism.　Evaluation　on　four　public　datasets,　including　METR-LA　and　PEMS-BAY,　demonstrates　that　the　proposed　model　outperforms　benchmark　models　such　as　MTGNN,　DCRNN,　and　AGCRN.　On　the　METR-LA　dataset,　our　model　reduces　the　MAE　by　4%　compared　to　MTGNN,　6.9%　compared　to　DCRNN,　and　5.8%　compared　to　AGCRN,　confirming　its　efficacy　in　traffic　flow　prediction.