Building　type　information　indicates　the　functional　properties　of　buildings　and　plays　a　crucial　role　in　smart　city　development　and　urban　socio-economic　activities.　Existing　methods　for　classifying　building　types　often　face　challenges　in　accurately　distinguishing　buildings　between　types　while　maintaining　well-delineated　boundaries,　especially　in　complex　urban　environments.　This　study　introduces　a　novel　framework,　i.e.,　CNN-Transformer　cross-attention　feature　fusion　network　(CTCFNet),　for　building　type　classification　from　very-high-resolution　remote　sensing　images.　CTCFNet　integrates　CNNs　and　Transformers　using　an　interactive　cross-encoder　fusion　(ICEF)　module　that　enhances　semantic　feature　learning　and　improves　classification　accuracy　in　complex　scenarios.　We　develop　an　adaptive　collaboration　optimization　(ACO)　module　that　applies　human　visual　attention　mechanisms　to　enhance　the　feature　representation　of　building　types　and　boundaries　simultaneously.　To　address　the　scarcity　of　datasets　in　building　type　classification,　we　create　two　new　datasets:　the　urban　building　type　(UBT)　dataset　and　the　town　building　type　(TBT)　dataset,　for　model　evaluation.　Extensive　experiments　on　these　datasets　demonstrate　that　CTCFNet　outperforms　popular　CNNs,　Transformers,　and　dual-encoder　methods　in　identifying　building　types　across　various　regions,　achieving　the　highest　MIoU　of　78.20%　and　77.11%,　F1　scores　of　86.83%　and　88.22%,　and　OA　of　95.07%　and　95.73%　on　the　UBT　and　TBT　datasets,　respectively.　We　conclude　that　CTCFNet　effectively　addresses　the　challenges　of　high　interclass　similarity　and　intraclass　inconsistency　in　complex　scenes,　yielding　results　with　well-delineated　building　boundaries　and　accurate　building　types.　The　codes　and　datasets　in　this　article　are　accessible　at　https://github.com/zsfaff/CTCFNet.　　©　2008-2012　IEEE.