In　maritime　logistics　optimization,　considerable　research　efforts　are　focused　on　the　extraction　of　deep　behavioral　characteristics　from　comprehensive　shipping　data　to　discern　patterns　in　maritime　vessel　behavior.　The　effective　linkage　of　these　characteristics　with　maritime　infrastructure,　such　as　berths,　is　critical　for　the　enhancement　of　ship　navigation　systems.　This　endeavor　is　paramount　not　only　as　a　research　focus　within　maritime　information　science　but　also　for　the　progression　of　intelligent　maritime　systems.　Traditional　methodologies　have　primarily　emphasized　the　analysis　of　navigational　paths　of　vessels　without　an　extensive　consideration　of　the　geographical　dynamics　between　ships　and　port　infrastructure.　However,　the　introduction　of　knowledge　graphs　has　enabled　the　integration　of　disparate　data　sources,　facilitating　new　insights　that　propel　the　development　of　intelligent　maritime　systems.　This　manuscript　presents　a　novel　framework　using　knowledge　graph　technology　for　profound　analysis　of　maritime　data.　Utilizing　automatic　identification　system　(AIS)　data　alongside　spatial　information　from　port　facilities,　the　framework　forms　semantic　triplet　connections　among　ships,　anchorages,　berths,　and　waterways.　This　enables　the　semantic　modeling　of　maritime　behaviors,　offering　precise　identification　of　ships　through　their　diverse　semantic　information.　Moreover,　by　exploiting　the　semantic　relations　between　ships　and　berths,　a　reverse　semantic　knowledge　graph　for　berths　is　constructed,　which　is　specifically　tailored　to　ship　type,　size,　and　category.　The　manuscript　critically　evaluates　a　range　of　graph　embedding　techniques,　dimensionality　reduction　methods,　and　classification　strategies　through　experimental　frameworks　to　determine　the　most　efficacious　methodologies.　The　findings　reveal　that　the　maritime　knowledge　graph　significantly　enhances　the　semantic　understanding　of　unmanned　maritime　equipment,　thereby　improving　decision-making　capabilities.　Additionally,　it　establishes　a　semantic　foundation　for　the　development　of　expansive　maritime　models,　illustrating　the　potential　of　knowledge　graph　technology　in　advancing　intelligent　maritime　systems.