In　the　extensive　monitoring　of　maritime　traffic,　maritime　management　frequently　encounters　incomplete　automatic　identification　system　(AIS)　data.　This　deficiency　poses　significant　challenges　to　safety　management,　requiring　effective　methods　to　infer　corresponding　ship　information.　We　tackle　this　issue　using　a　classification　approach.　Due　to　the　absence　of　a　fixed　road　network　at　sea　unlike　on　land,　raw　trajectories　are　difficult　to　convert　and　cannot　be　directly　fed　into　neural　networks.　We　devised　a　latitude-longitude　gridding　encoding　strategy　capable　of　transforming　continuous　latitude-longitude　data　into　discrete　grid　points.　Simultaneously,　we　employed　a　compression　algorithm　to　further　extract　significant　grid　points,　thereby　shortening　the　encoding　sequence.　Utilizing　natural　language　processing　techniques,　we　integrate　the　Word2vec　word　embedding　approach　with　our　novel　biLSTM　self-attention　chunk-max　pooling　net　(biSAMNet)　model,　enhancing　the　classification　of　vessel　trajectories.　This　method　classifies　targets　into　ship　types　and　ship　lengths　within　static　information.　Employing　the　Taiwan　Strait　as　a　case　study　and　benchmarking　against　CNN,　RNN,　and　methods　based　on　the　attention　mechanism,　our　findings　underscore　our　model＇s　superiority.　The　biSAMNet　achieves　an　impressive　trajectory　classification　F1　score　of　0.94　in　the　ship　category　dataset　using　only　five-dimensional　word　embeddings.　Additionally,　through　ablation　experiments,　the　effectiveness　of　the　Word2vec　pre-trained　embedding　layer　is　highlighted.　This　study　introduces　a　novel　method　for　handling　ship　trajectory　data,　addressing　the　challenge　of　obtaining　ship　static　information　when　AIS　data　are　unreliable.