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.