In　the　field　of　multi-style　textile　defect　detection,　a　common　challenge　is　the　difficulty　of　adapting　the　inherent　detection　model　to　different　styles　of　textile　defects.　Changes　in　the　color　or　style　of　the　textile　often　result　in　a　decrease　in　the　accuracy　of　defect　detection.　Relying　solely　on　the　model　for　fine-tuning　inspections　can　lead　to　catastrophic　forgetting,　which　significantly　impacts　the　performance　of　the　textile　defect　detector.　To　address　these　challenges,　a　multi-task　correlation　distillation　(MTCD)　anomaly　detection　method　based　on　knowledge　distillation　and　representative　sampling　is　proposed　to　detect　multi-style　textile　defects.　To　enable　MTCD　to　detect　defects　of　new-style　textiles　while　maintaining　the　detection　of　old-style　textiles,　two　main　modules　are　introduced.　The　distillation　adaptation　module　(DAM)　explores　the　intra-feature　correlation　in　the　feature　space　of　the　target　detector,　allowing　the　student　model　to　acquire　knowledge　of　new-style　textile　defect　detection　while　inheriting　the　teacher　model＇s　detection　ability　for　old-style　textile　defects.　The　representative　sampling　module　(RSM)　stores　representative　knowledge　of　textile　defect　detection　for　old-style　textiles,　facilitating　the　transfer　of　knowledge　learned　from　detecting　new-style　textile　defect　styles　and　maintaining　the　ability　to　detect　defects　in　old-style　textiles.　This　increases　the　detection　accuracy　of　the　student　model　for　new-style　textile　defects.　The　results　show　that　the　proposed　MTCD　method　can　adapt　to　the　new　textile　defect　detection　while　maintaining　the　accuracy　of　the　old　textile　defect　detection　and　avoiding　the　problem　of　catastrophic　forgetting.　Furthermore,　it　offers　a　better　balance　between　stability　and　plasticity,　making　it　a　promising　solution　for　defect　detection　of　multi-style　textiles　in　industrial　production　environments.　　©　2024　SPIE　and　IS&T.