Fuzzy　decision　trees　(FDTs)　is　one　of　the　considerably　excellent　methods.　Most　of　the　existing　FDTs＇　methods　are　oriented　to　classification　tasks.　Applying　FDTs　to　regression　tasks　may　solve　complex　industrial　modeling　problems.　In　this　article,　we　propose　the　Takagi-Sugeno　(T-S)　fuzzy　regression　tree　(TSFRT),　which　uses　the　hypothesis　of　＂feature　screening　followed　by　T-S　fuzzy　reasoning.＂　In　the　TSFRT,　the　growth　process　(crisp　set　theory)　can　be　deemed　as　feature　screening,　and　each　leaf　node　(fuzzy　set　theory)　is　viewed　as　a　T-S　inference　reasoning　system.　Thus,　the　TSFRT　becomes　a　top-down　structure.　We　develop　multiple　strategies　to　identify　the　parameters　of　the　T-S　system　in　the　leaf　node　using　sample-by-sample　and　batch　samples.　To　improve　the　method＇s　generalization　performance,　we　also　generalize　an　ensemble　method　with　pseudoinverse　and　ridge　regression.　The　proposed　methods　are　evaluated　by　several　high-　and　low-dimensional　complex　industrial　processes.　The　experimental　results　show　that　the　proposed　method　remarkably　outperforms　other　popular　regression　methods.