Artificial　intelligence　(AI)　has　played　a　key　role　in　advancing　autonomous　navigation　for　unmanned　ships,　where　ship　trajectory　prediction　is　crucial　for　ensuring　maritime　safety.　As　the　shipping　industry　grows　and　the　number　of　ships　increases,　especially　with　autonomous　ships　operating　in　complex　environments,　collision　risks　have　become　a　major　concern.　Accurate　trajectory　prediction,　supported　by　advanced　AI　techniques,　is　crucial　for　the　safe　operation　of　these　ships.　While　current　models　predict　ship　trajectories　with　high　precision　using　Automatic　Identification　System　(AIS)　data,　they　often　fail　to　incorporate　prior　knowledge　of　collision　risks　and　struggle　to　model　ship　interactions　that　could　lead　to　collisions.　To　overcome　these　limitations,　the　DGCN-Transformer　(Dynamic　Graph　Convolution　Network-Transformer)　model　is　proposed.　This　model　enhances　the　accuracy　and　reliability　of　ship　trajectory　predictions　by　incorporating　collision　risk　modeling　into　the　prediction　framework.　It　uses　the　Quaternion　Ship　Domain　(QSD)　to　model　potential　collision　scenarios,　integrating　an　advanced　understanding　of　ships＇　spatial　and　kinematic　properties.　The　model　integrates　QSD-based　prior　knowledge　into　an　advanced　Graph　Convolutional　Network　(GCN)　for　spatial　modeling,　while　the　Transformer　component　captures　and　analyzes　temporal　features,　overcoming　the　limitations　of　traditional　Long　Short-Term　Memory　(LSTM)　networks.　Experiments　with　AIS　data　from　Tianjin,　Caofeidian,　and　Chengshanjiao　ports　demonstrate　that　the　DGCN-Transformer　model　outperforms　state-of-the-art　models,　significantly　improving　trajectory　prediction　accuracy.　Specifically,　at　Tianjin　Port,　the　DGCN-Transformer　model　reduces　Final　Displacement　Error　(FDE)　by　36.1%,　Maximum　Displacement　Error　(MDE)　by　15.4%,　and　Average　Displacement　Error　(ADE)　by　50%　compared　to　the　best　baseline　model,　highlighting　the　model＇s　effectiveness　in　enhancing　the　safety　of　autonomous　ship　navigation.　©　2025　Elsevier　Ltd