From　the　perspective　of　optimizing　maritime　logistics,　a　key　focus　in　the　field　of　maritime　information　research　has　been　how　to　extract　behavioral　patterns　and　deep　behavioral　characteristics　of　vessels　from　vast　amounts　of　shipping　statistics.　Additionally,　aligning　these　characteristics　with　infrastructure　such　as　berths　for　effective　association　and　recommendation　to　vessels　is　a　critical　requirement　for　the　evolution　of　intelligent　maritime　systems.　Traditional　methods　primarily　focus　on　the　behavioral　trajectories　of　vessel　navigation,　failing　to　explore　the　geographical　interconnections　between　vessels　and　port　infrastructure.　In　light　of　this,　this　paper　proposes　a　framework　for　deep　mining　of　shipping　information　based　on　knowledge　graph　technology.　Utilizing　AIS　data　and　spatial　data　of　port　facilities,　it　constructs　a　semantic　relationship　in　the　form　of　triplets　between　vessels,　berths,　and　waterways,　and　semantically　models　vessel　behaviors.　Effective　identification　of　vessels　is　achieved　based　on　various　semantic　information.　Simultaneously,　based　on　the　berthing　semantic　relationship　between　vessels　and　berths,　a　reverse　semantic　knowledge　graph　of　berths　is　constructed　with　respect　to　vessel　type,　size,　and　class.　This　study　compares　different　graph　embedding　methods,　dimensionality　reduction　techniques,　and　classification　approaches　to　achieve　optimal　experimental　results.　The　findings　indicate　that　the　vessel　type　recognition　accuracy　in　the　proposed　framework　reached　83.1%,　and　the　number　of　Identical　Relationships　between　the　recommended　and　original　berths　in　similar　berth　recommendations　was　3.755.　The　experiments　demonstrate　that　the　framework　can　provide　a　technical　foundation　for　deep　mining　of　vessel　behavior,　vessel　type　identification,　and　berth　recommendation,　as　well　as　a　semantic　basis　for　large-scale　maritime　models.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.