Spacecraft　component　recognition　is　crucial　for　tasks　such　as　on-orbit　maintenance　and　space　docking,　aiming　to　identify　and　categorize　different　parts　of　a　spacecraft.　Semantic　segmentation,　known　for　its　excellence　in　instance-level　recognition,　precise　boundary　delineation,　and　enhancement　of　automation　capabilities,　is　well-suited　for　this　task.　However,　applying　existing　semantic　segmentation　methods　to　spacecraft　component　recognition　still　encounters　issues　with　false　detections,　missed　detections,　and　unclear　boundaries　of　spacecraft　components.　In　order　to　address　these　issues,　we　propose　a　multiscale　adaptively　spatial　feature　fusion　network　(MASFFN)　for　spacecraft　component　recognition.　The　MASFFN　comprises　a　spatial　attention-aware　encoder　(SAE)　and　a　multiscale　adaptively　spatial　feature　fusion-based　decoder　(Multi-ASFFD).　First,　the　spatial　attention-aware　feature　fusion　module　within　the　SAE　integrates　spatial　attention-aware　features,　mid-level　semantic　features,　and　input　features　to　enhance　the　extraction　of　component　characteristics,　thus　improving　the　accuracy　in　capturing　size,　shape,　and　texture　information.　Second,　the　multi-scale　adaptively　spatial　feature　fusion　module　within　the　Multi-ASFFD　cascades　four　adaptively　spatial　feature　fusion　blocks　to　fuse　low-level,　middle-level,　and　high-level　features　at　various　scales　to　enrich　the　semantic　information　for　different　spacecraft　components.　Finally,　a　compound　loss　function　comprising　the　cross-entropy　and　boundary　losses　is　presented　to　guide　the　MASFFN　better　focus　on　the　unclear　component　edge.　The　proposed　method　has　been　validated　on　the　UESD　and　URSO　datasets,　and　the　experimental　results　demonstrate　the　superiority　of　MASFFN　over　existing　spacecraft　component　recognition　methods.