The　intelligent　maritime　transportation　system　has　emerged　as　a　pivotal　component　in　port　management,　owing　to　the　rapid　advancements　in　artificial　intelligence　and　big　data　technology.　Its　essence　lies　in　the　application　of　digital　modeling　techniques,　which　leverage　extensive　ship　data　to　facilitate　efficient　operations.　In　this　regard,　effective　modeling　and　accurate　prediction　of　the　fluctuation　patterns　of　ship　traffic　in　multiple　port　regions　will　provide　data　support　for　trade　analysis,　port　construction　planning,　and　traffic　safety　management.　In　order　to　better　express　the　potential　interdependencies　between　ports,　inspired　by　graph　neural　networks,　this　paper　proposes　a　data-driven　approach　to　construct　a　multi-port　network　and　designs　a　spatiotemporal　graph　neural　network　model.　The　model　incorporates　graph　attention　networks　and　a　dilated　causal　convolutional　architecture　to　capture　the　temporal　and　spatial　dimensions　of　traffic　variation　patterns.　It　also　employs　a　gated-mechanism-based　spatiotemporal　bi-dimensional　feature　fusion　strategy　to　handle　the　potential　unequal　relationships　between　the　two　dimensions　of　features.　Compared　to　existing　methods　for　port　traffic　prediction,　this　model　fully　considers　the　network　characteristics　of　the　overall　port　and　fills　the　research　gap　in　multi-port　scenarios.　In　the　experiments,　real　port　ship　traffic　datasets　were　constructed　using　data　from　the　Automatic　Identification　System　(AIS)　and　port　geographical　information　data　for　model　validation.　The　results　demonstrate　that　the　model　exhibits　outstanding　robustness　and　performs　well　in　predicting　traffic　in　multiple　sub-regional　port　clusters.