It　is　challenging　to　investigate　semantic　change　detection　(SCD)　in　bi-temporal　high-resolution　(HR)　remote　sensing　images.　For　the　non-changing　surfaces　in　the　same　location　of　bi-temporal　images,　existing　SCD　methods　often　obtain　the　results　with　frequent　errors　or　incomplete　change　detection　due　to　insufficient　performance　on　overcoming　the　phenomenon　of　intraclass　differences.　To　address　the　above-mentioned　issues,　we　propose　a　novel　multi-task　consistency　enhancement　network　(MCENet)　for　SCD.　Specifically,　a　multi-task　learning-based　network　is　constructed　by　combining　CNN　and　Transformer　as　the　backbone.　Moreover,　a　multi-task　consistency　enhancement　module　(MCEM)　is　introduced,　and　cross-task　mapping　connections　are　selected　as　auxiliary　designs　in　the　network　to　enhance　the　learning　of　semantic　consistency　in　non-changing　regions　and　the　integrity　of　change　features.　Furthermore,　we　establish　a　novel　joint　loss　function　to　alleviate　the　negative　effect　of　class　imbalances　in　quantity　during　network　training　optimization.　We　performed　experiments　on　publicly　available　SCD　datasets,　including　the　SECOND　and　HRSCD　datasets.　MCENet　achieved　promising　results,　with　a　22.06%　Sek　and　a　37.41%　Score　on　the　SECOND　dataset　and　a　14.87%　Sek　and　a　30.61%　Score　on　the　HRSCD　dataset.　Moreover,　we　evaluated　the　applicability　of　MCENet　on　the　NAFZ　dataset　that　was　employed　for　cropland　change　detection　and　non-agricultural　identification,　with　a　21.67%　Sek　and　a　37.28%　Score.　The　relevant　comparative　and　ablation　experiments　suggested　that　MCENet　possesses　superior　performance　and　effectiveness　in　network　design.