Accompanied　by　the　progress　and　development　of　artificial　intelligence　technology,　the　surface　unmanned　ship　plays　a　key　role　in　the　unmanned　control　system,　and　plays　a　key　role　in　data　acquisition　and　hydrological　survey.　In　the　process　of　practical　application,　the　stable　operation　of　the　unmanned　ship　will　be　greatly　affected　because　of　the　complex　operation　and　the　unknown　environment,　so　it　is　very　necessary　to　predict　its　bits.　Accurately　predicting　the　movement　of　unmanned　vessels　is　critical　for　navigation,　safety　monitoring　and　mission　planning,　and　can　significantly　improve　their　stability　and　operational　efficiency　in　complex　waters.　This　paper　introduces　the　variational　modal　decomposition　(VMD)　algorithm　and　proposes　a　combined　prediction　model　(VMD-NGO-LSTM)　based　on　the　Northern　Goshawk　Optimisation　Algorithm　(NGO)　and　Bidirectional　Long　and　Short-Term　Storage　Memory　Network　(BiLSTM),　which　is　found　to　be　highly　accurate　by　processing　the　prediction　of　unmanned　boat　navigation　data　in　Xiyuan　River.　It　reflects　a　faster　training　speed　when　processing　large　unmanned　boat　data,　and　has　a　certain　reference　value　for　the　position　adjustment　of　unmanned　boats.　©　2024　SPIE.