Semantic　change　detection　(SCD)　can　recognize　the　region　and　the　type　of　changes　in　remote　sensing　images.　Existing　methods　are　either　based　on　transformer　or　convolutional　neural　network　(CNN),　but　due　to　the　size　of　various　ground　objects　is　different,　it　is　necessary　to　have　global　modeling　ability　and　local　information　extraction　ability　at　the　same　time.　Therefore,　in　this　paper　we　propose　a　fusion　semantic　change　detection　network　(FSCD)　with　both　global　modeling　ability　and　local　information　extraction　ability　by　fusing　transformer　and　CNN.　A　semi-parallel　fusion　block　has　also　been　proposed　to　construct　FSCD.　It　can　not　only　have　global　and　local　features　in　parallel,　but　also　fuse　them　as　deeply　as　serial.　To　better　adaptively　decide　which　mechanism　is　applied　to　which　pixel,　we　design　a　self-attention　and　convolution　selection　module　(ACSM).　ACSM　is　a　selfattention　mechanism　used　to　selectively　combine　transformer　and　CNN.　Specifically,　the　importance　of　each　mechanism　is　automatically　obtained　by　learning.　According　to　the　importance,　the　mechanism　suitable　for　a　pixel　is　selected,　which　is　better　than　using　either　mechanism　alone.　We　evaluate　the　proposed　FSCD　on　two　datasets,　and　the　proposed　network　has　a　significant　improvement　compared　with　the　state-of-the-art　network.