To　address　the　issues　of　complex　backgrounds　and　poor　segmentation　performance　for　small　ship　objects　in　sea-land　port　areas,　we　propose　a　sea-land　port　segmentation　algorithm　based　on　spatial　and　semantic　alignment　fusion.　The　algorithm　utilizes　parallel　Transformer-convolutional-neural-network　(CNN)　dual-branch　encoders　for　feature　extraction　and　introduces　two　modules:　spatial　alignment　fusion　and　semantic　alignment　fusion.　By　the　collaborative　work　of　four　submodules:　spatial　feature　alignment,　spatial　feature　fusion,　semantic　feature　alignment,　and　semantic　feature　fusion,　the　dual-branch　network　achieves　feature　alignment　and　fusion.　The　spatial　and　semantic　alignment　fusion　module　efficiently　combines　local　details　extracted　by　the　Transformer-CNN　dual-branch　with　global　semantic　information.　This　enhances　the　model＇s　ability　to　understand　and　analyze　complex　sea-land　port　scenes,　effectively　addressing　low　segmentation　accuracy　of　port　ship　objects　and　the　overlapping　and　occlusion　of　port　objects.　Experimental　results　demonstrate　that　the　proposed　sea-land　port　segmentation　algorithm　achieves　optimal　segmentation　accuracy　on　two　publicly　available　sea-land　port　segmentation　datasets,　ISDSD　and　HRSC2016-SL.