Anoectochilus　roxburghii　from　different　origins　has　different　nutritional　content　and　different　price.　Achieving　origin　identification　is　of　great　significance　to　the　development　and　standardization　of　the　Anoectochilus　roxburghii　industry　However,　it　is　influenced　by　complex　factors,　including　the　specific　strain　and　growth　environment.　Achieving　a　high　accuracy　in　origin　identification　presents　significant　challenges　and　may　not　always　meet　the　stringent　requirements.　To　account　for　the　unique　characteristics　of　Anoectochilus　roxburghii　dataset　from　different　origins,　such　as　limited　sample　size,　imbalanced　samples,　and　numerous　sample　interference　factors,　a　method　based　on　improved　SMOTE　and　CatBoost　is　designed　to　address　the　need　for　precise　origin　identification.　First,　a　Fourier　transform　near　infrared　spectrometer　was　used　to　collect　sample　information　of　Anoectochilus　roxburghii　from　three　different　origins,　and　then　the　improved　SMOTE　algorithm　was　used　to　balance　the　dataset.　Finally,　CatBoost　classifier　was　used　to　identify　the　different　origins.　Comparative　experimental　results　show　that　the　method　proposed　in　this　article　has　the　highest　identification　accuracy,　reaching　more　than　97%,　which　is　6.9%　and　2.8%　higher　than　using　original　data　and　the　original　SMOTE　algorithm　respectively.　The　model　constructed　can　efficiently　identify　Anoectochilus　roxburghii　of　different　origins　and　can　be　served　as　a　useful　reference　for　quality　supervision　of　Anoectochilus　roxburghii.　©　2024　IEEE.