In　the　process　of　monitoring　a　myriad　of　vessels,　maritime　administrators　recurrently　confront　situations　where　crucial　static　fields,　such　as　vessel　type,　are　frequently　missing　from　automatic　identification　system　(AIS)　data.　This　omission　presents　a　significant　obstacle　to　effective　safety　management,　prompting　the　need　for　a　method　to　rectify　incomplete　data.　In　terrestrial　traffic,　constraints　dictated　by　road　widths　do　not　apply　to　marine　traffic,　thus　allowing　vessels　of　an　identical　type　to　opt　for　routes　that　diverge　by　numerous　nautical　miles.　This　condition　renders　trajectory　clustering　insufficient.　To　tackle　this　issue,　this　paper　proposes　a　division　of　the　sea　area　into　spatiotemporal　grids　and　a　transformation　of　vessel　trajectories　into　a　coded　sequence　of　navigational　grids.　Word2vec　embedding　is　merged　with　deep　neural　networks　predicated　on　convolutional　neural　networks　(CNNs),　recurrent　neural　networks　(RNNs),　and　attention　mechanisms　through　the　utilization　of　natural　language　processing　techniques.　The　goal　is　to　facilitate　efficient　vessel　classification　via　deep　learning.　Applying　the　Taiwan　Strait　as　a　case　study,　it　is　shown　that　under　the　conditions　of　a　five-dimensional　word　embedding,　the　F1　score　for　vessel　classification　using　RNN　models　can　attain　0.94.　This　investigation　provides　a　unique　method　for　processing　vessel　trajectory　data,　and　it　also　assists　in　the　effective　completion　of　missing　vessel　types.　　©　2023　IEEE.