There is a certain relationship between the quality of fresh tea leaves and the elevation of growing, but at present, it is no effective method to discriminate the elevation of fresh leaves picked. In this study, fresh tea leaves of different elevation were used as the research objects, after near infrared spectroscopy scanned and the characteristic spectral interval selected, the prediction models of elevation of fresh tea leaves were established by stepwise multiple linear regression (SMLR), principal component regression (PCR) and synergy interval partial least squares (Si PLS). The results showed that, the correlation coefficient and root mean square error of prediction set was respectively 0.800 5 and 0.486 by SMLR method, which used the spectroscopy in the range of 5 542.41-6 888.48 cm-1 and the first derivative +3 point Norris smoothing pretreatment; the correlation coefficient and root mean square error of prediction set was respectively 0.803 6 and 0.472 by PCR method, which used the spectroscopy in the range of 4 929.16-6 965.62 cm-1 and the first derivative + 3 point Norris smoothing pretreatment; the correlation coefficient and root mean square error of prediction set was respectively 0.944 3 and 0.295 by Si PLS method, which contained 18 spectral intervals combined with \[5 8 11 17\] of four subintervals and 13 factors. By comparison, the Si PLS model has the best prediction results. It was preliminary realized to discriminate the elevation of fresh tea leaf samples rapidly and nondestructively by using NIRSSiPLS method.