Change　detection　in　remote　sensing　images　is　crucial　for　assessing　human　activity　impacts　and　supporting　government　decision-making.　However,　in　practice,　obtaining　bitemporal　remote　sensing　images　with　consistent　conditions　is　highly　limited,　and　existing　change　detection　methods　still　face　two　main　challenges:　1)　in　real-world　scenarios,　inconsistent　sensor　and　lighting　conditions　cause　significant　style　(visual　appearance)　differences　between　bitemporal　remote　sensing　images,　leading　to　false　changes　and　reducing　change　detection　accuracy　and　2)　remote　sensing　images　contain　complex　semantic　information,　and　complex　scenarios　such　as　shadow　occlusion　and　seasonal　vegetation　changes　make　the　existing　methods　difficult　to　capture　relevant　features　related　to　change　areas.　To　address　these　challenges,　we　propose　a　style　consistency　enhanced　differential　network　(SCEDNet)　to　eliminate　style　discrepancies　between　temporally　distinct　images　and　enhance　the　semantic　information　of　change　features.　Specifically,　we　introduce　a　style　consistency　module　(SCM)　in　the　encoder　to　extract　consistent　features　by　computing　the　mean　and　variance　of　temporal　features.　Then,　we　introduce　an　enhanced　differential　module　(EDM)　to　enhance　change　semantics,　tackling　issues　such　as　mislocalization　and　incomplete　regions　in　complex　cases　such　as　shadow　occlusion　and　seasonal　vegetation　changes.　In　addition,　we　design　a　gate　fusion　upsampling　(GFU)　and　change　refine　module　(CRM)　in　the　decoder　to　integrate　multilevel　differential　features　with　different　semantic　information　and　highlight　key　changes,　further　improving　change　detection　performance.　Experiments　on　the　CDD　and　GZ_CD　datasets　show　that　SCEDNet　outperformed　eight　methods,　achieving　F1-scores　of　95.59%　and　90.41%,　respectively.　Code　and　datasets　are　available　at　https://github.com/Yzwfff/SCEDNet