Anoectochilus　roxburghii　(Wall.)　Lindl.　(Orchidaceae)　is　a　rare　traditional　Chinese　medicine.　For　seeking　high　profit,　some　traditional　Chinese　medicine　sellers　usually　adulterated　A.　roxburghii　with　Goodyera　Schlechtendaliana　and　Ludisia　discolor　or　directly　fake　A.　roxburghii　using　Anoectochilus　formosanus.　These　counterfeits　with　similar　appearance　greatly　influence　the　prescription　efficacy.　Therefore,　there　is　an　urgent　need　for　an　effective　and　fast　authentication　method　to　identify　A.　roxburghii　and　its　counterfeits.　In　this　paper,　the　near-infrared　spectroscopy　(NIRS)　data　of　A.　roxburghii　and　its　counterfeits　are　mearsured.　Then,　an　improved　inception　architecture　based　1-dimensional　convolutional　neural　network　(Improved　1D-Inception-CNN)　is　designed　for　processing　the　NIRS　data　and　identifying　A.　roxburghii　and　its　counterfeits.　The　Improved　1D-Inception-CNN　has　less　parameters　and　high　calculation　efficiency　which　makes　the　identification　model　more　practical.　The　experimental　results　show　that　compared　with　traditional　structured　CNN　models,　the　complexity　of　the　Improved　1D-Inception-CNN　is　reduced　by　40　%,　the　parameters　are　reduced　by　50　%　and　the　performances　are　improved　by　1.01　%.　Therefore,　the　Improved　1D-Inception-CNN　model　based　on　NIRS　technology　can　effectively　and　quickly　identify　A.　roxburghii　and　its　counterfeits.　(c)　2021　Elsevier　B.V.　All　reserved.