Predicting　the　spatiotemporal　dynamics　of　land　cover　and　its　carbon　stock　holds　significant　importance　in　guiding　regional　sustainable　development,　enhancing　regional　carbon　stocks,　and　addressing　global　climate　change.　However,　there　is　insufficient　research　on　the　quantitative　relationships　between　various　land　cover　types　and　carbon　stock　changes,　as　well　as　their　future　spatial　predictions.　Focusing　on　the　core　area　of　Fuzhou　City,　China,　this　study　constructs　a　streamlined　framework　by　coupling　deep　learning　and　the　InVEST　model　to　predict　urban　land　cover　and　carbon　stock　changes　in　2025　and　2035.　The　results　show　that:　(1)　The　prediction　model　for　land　cover　change　has　high　applicability　and　can　produce　the　simulated　images　with　high　accuracy.　Impervious　surface　is　expected　to　increase　by　53　km(2)　in　2025　and　131　km(2)　in　2035　compared　to　2020,　resulting　in　considerable　reductions　in　forest　and　cropland.　(2)　Carbon　stocks　of　the　study　area　are　expected　to　decrease　by　1.68　x　10(6)t　in　2035　compared　to　2020　due　to　large　amounts　of　high-carbon-density　forests　and　croplands　being　converted　into　low-carbon-density　impervious　surfaces.　(3)　Multiple　regression　analysis　reveals　that　forests　have　the　largest　impact　on　carbon　stocks　in　the　area,　with　a　magnitude　5.25　times　greater　than　impervious　surfaces　and　11.5　times　greater　than　cropland,　whereas　impervious　surfaces　are　the　second　most　influential　land　cover　type　on　carbon　stock　changes.　Therefore,　expanding　forest　areas　becomes　an　essential　initiative　as　forests　could　offset　the　carbon　stock　loss　caused　by　impervious　surface　growth.　This　study　provides　scientific　references　for　optimizing　land-use　planning　and　formulating　policies　for　the　development　of　low-carbon　cities.