Spatiotemporal　Graph　(STG)　forecasting　is　an　essential　task　within　the　realm　of　spatiotemporal　data　mining　and　urban　computing.　Over　the　past　few　years,　Spatiotemporal　Graph　Neural　Networks　(STGNNs)　have　gained　significant　attention　as　promising　solutions　for　STG　forecasting.　However,　existing　methods　often　overlook　two　issues:　the　dynamic　spatial　dependencies　of　urban　networks　and　the　heterogeneity　of　urban　spatiotemporal　data.　In　this　paper,　we　propose　a　novel　framework　for　STG　learning　called　Dynamic　Meta-Graph　Convolutional　Recurrent　Network　(DMetaGCRN),　which　effectively　tackles　both　challenges.　Specifically,　we　first　build　a　meta-graph　generator　to　dynamically　generate　graph　structures,　which　integrates　various　dynamic　features,　including　input　sensor　signals　and　their　historical　trends,　periodic　information　(timestamp　embeddings),　and　meta-node　embeddings.　Among　them,　a　memory　network　is　used　to　guide　the　learning　of　meta-node　embeddings.　The　meta-graph　generation　process　enables　the　model　to　simulate　the　dynamic　spatial　dependencies　of　urban　networks　and　capture　data　heterogeneity.　Then,　we　design　a　Dynamic　Meta-Graph　Convolutional　Recurrent　Unit　(DMetaGCRU)　to　simultaneously　model　spatial　and　temporal　dependencies.　Finally,　we　formulate　the　proposed　DMetaGCRN　in　an　encoder–decoder　architecture　built　upon　DMetaGCRU　and　meta-graph　generator　components.　Extensive　experiments　on　four　real-world　urban　spatiotemporal　datasets　validate　that　the　proposed　DMetaGCRN　framework　outperforms　state-of-the-art　approaches.　©　2024　Elsevier　Ltd