Near-surface　air　temperature　(Ta)　plays　a　critical　role　in　land-atmosphere　interactions,　influencing　human　health,　agricultural　productivity,　and　ecosystem　stability.　Urban　functional　zones　(UFZs),　with　distinct　social　and　economic　roles,　exhibit　unique　temperature　characteristics　shaped　by　variations　in　land　use,　building　density,　and　spatial　configuration,　thereby　significantly　affecting　Ta　distribution　in　urban　areas.　This　study　introduces　a　Functional-Spatial-Temporal　Graph　Convolutional　Network　(FST-GCN)　model,　enhanced　with　a　spatial-temporal　attention　mechanism,　for　precise　estimation　of　Ta　across　UFZs.　By　integrating　high-resolution　remote　sensing　imagery　and　multi-source　meteorological　data,　the　model　was　applied　to　estimate　Ta　in　thirteen　major　Chinese　cities　from　2015　to　2021.　The　model＇s　performance　was　evaluated　using　five-fold　cross-validation　against　meteorological　station　data,　achieving　a　mean　absolute　error　(MAE)　of　approximately　1.44　°C　and　a　coefficient　of　determination　(R²)　exceeding　0.9,　demonstrating　significant　improvements　over　traditional　methods.　The　analysis　reveals　a　strong　dependency　of　temperature　characteristics　on　UFZ　types　and　seasonal　variations.　High-density　urban　zones,　such　as　industrial　and　commercial　areas,　exhibit　higher　temperatures,　particularly　during　warmer　months,　due　to　factors　like　intensified　human　activity　and　limited　vegetation　cover.　Conversely,　green　zones　and　residential　areas　show　a　notable　cooling　effect,　emphasizing　the　critical　role　of　urban　greenery　in　mitigating　heat　retention.　Additionally,　distinct　seasonal　temperature　variations　are　observed　between　northern　and　southern　cities,　driven　by　regional　climatic　differences　and　urban　spatial　configurations.　©　2025　Elsevier　Ltd