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.　©　2008-2012　IEEE.