Port　traffic　flow　modeling　based　on　big　data　is　an　important　research　direction　in　the　shipping　field,　having　the　task　of　traffic　forecasting　for　ports　worldwide.　Graph　neural　networks　have　a　strong　ability　to　capture　the　spatial　topology　characteristics　and　may　be　combined　with　recurrent　neural　networks　or　dilated　convolution　methods　in　time　series　prediction,　producing　a　large　number　of　spatiotemporal　graph　convolution　models.　Such　models　have　been　widely　and　successfully　applied　in　traffic　forecasting.　Differing　from　urban　traffic　flow　data,　the　statistical　time　span　of　port　vessel　flow　and　throughput　data　is　large,　its　spatial　span　is　wide,　and　the　data　experience　significant　fluctuations.　Consequently,　certain　spatiotemporal　graph　convolution　traffic　prediction　models　are　unsuitable　for　shipping　scenarios.　To　address　this　shortcoming,　we　have　created　a　unique　port　flow　dataset　based　on　automatic　identification　system　(AIS)　and　port　geographic　data.　Using　theoretical　analysis　and　experimental　comparison,　we　have　determined　the　most　appropriate　model　for　shipping　predictions　based　on　existing　spatiotemporal　graph　models　and　have　proposed　model　optimization　recommendations　for　the　maritime　domain.　Our　experiment　based　on　an　open　source　traffic　forecasting　framework　to　compare　the　results　of　multiple　existing　spatiotemporal　graph　models　under　fair　conditions　with　the　central　ports　of　Rotterdam,　Shanghai,　Boston,　and　Singapore.　The　results　show　that　Graph　WaveNet　exhibits　better　performance　in　shipping　scenarios.