Semantic　Change　Detection　(SCD)　in　Remote　Sensing　Images　(RSI)　aims　to　identify　changes　in　the　type　of　Land　Cover/Land　Use　(LCLU).　The　＂from-to＂　information　of　the　acquired　image　has　more　profound　practical　significance　than　Binary　Change　Detection　(BCD).　However,　most　deep　learning-based　SCD　algorithms　do　not　fully　exploit　the　spatial-temporal　information　of　multilevel　features,　leading　to　challenges　in　extracting　LCLU　features　in　complex　scenes.　To　address　these　issues,　we　propose　a　Spatial-Temporal　Semantic　Feature　Interaction　Network　(STS-FINet)　to　improve　the　performance　of　SCD　in　RSI.　The　proposed　STS-FINet　comprises　a　Multi-Scale　Feature　Extraction　Encoder　(MS-FEE),　a　Transformer-based　Multilevel　Feature　Interaction　module　(TML-FI),　and　a　Multilevel　Feature　Fusion　Decoder　(ML-FFD).　The　MS-FEE　extracts　deep　semantic　and　differential　information　from　the　RSI.　The　TML-FI　is　designed　to　mine　the　spatial-temporal　information　by　extracting　long-range　dependencies　and　spatial　information　from　multilevel　features　to　improve　spatial　perception.　Moreover,　Mixed　Spatial　Reasoning　Convolution　block　(MixSrc)　is　presented　to　enrich　the　spatial　information　by　extracting　the　multiscale　features,　thus　improving　the　model＇s　capability　to　interpret　complex　scenes.　Finally,　ML-FFD　integrates　the　multilevel　features,　resulting　in　the　generation　of　the　semantic　change　map.　The　effectiveness　of　the　proposed　STS-FINet　is　verified　on　two　high-resolution　RSI　datasets.　Experimental　results　show　that　the　proposed　STS-FINet　achieves　better　change　detection　performance　than　SOTA　methods.