Tetrastigma　hemsleyanum,　a　rare　medicinal　herbs　in　China,　contains　many　kinds　of　curative　effects.　However,　the　content　of　active　ingredients　of　T.　hemsleyanum　from　different　places　is　remarkablely　different.　So,　it　is　necessary　to　discriminate　this　promising　medicinal　T.　hemsleyanum　from　different　places.　In　this　work,　spectra　of　T.　hemsleyanum　collected　from　Zhejiang,　Yunnan,　Anhui,　Guangxi　and　Hubei　provinces　were　recorded　with　Fourier　transform　near　infrared　spectroscopy,　ranging　from　10　000　to　4　000　cm　-1　.　And　the　identification　algorithm　was　applied　to　effectively　identify　the　T.　hemsleyanum　from　the　known　origin　and　other　new　places　because　the　spectral　data　of　T.　hemsleyanum　is　not　sufficient.　Hence,　in　this　study,　three　improvements　of　kernel　density　estimation　algorithm　have　been　achieved　to　identify　T.　hemsleyanum:　(1)　estimate　the　probability　density　of　the　samples　via　the　perspective　of　distance;　(2)　calculate　the　bandwidth　parameters　by　training　the　credibility　of　samples;　(3)　propose　a　recognition　method　based　on　probability　density　function　of　training　set　samples　to　recognize　unknown　origin.　The　identifying　accuracy　of　training　set　sample　and　prediction　set　by　the　algorithm　were　reached　100%　and　97.8%,　respectively.　Additionally,　the　new　places　of　T.　hemsleyanum　can　be　accurately　identified　used　the　algorithm.　The　results　show　that　the　improved　algorithm　based　on　kernel　density　estimation　can　effectively　identify　T.　hemsleyanum,　and　recognize　the　unknown　origin　samples.　©　2018,　Peking　University　Press.　All　right　reserved.