As a bridge between the synthesis and decomposition of a biological organisms, plant litter impacts the structure, function and key ecological processes of terrestrial ecosystems through material, energy and information flow. Litters decompose as species mixtures in natural systems, especially in species-rich subtropical evergreen forests. It is difficult to accurately identify leaf litter for non-professionals due to complex tree species in the field. Besides, misidentifications cause many problems for thesubsequent litter decomposition research. As a fast and nondestructive analysis method, near-infrared spectroscopy has been successfully applied to identify boletus, citrus and rice. The technique mentioned above systems provided a new way to solve problems of leaf litter identification. In this study, 540 leaf litter samples of 6 dominant tree species of typical mid-mountain moist evergreen broad-leaved forests in the Mts. Ailaoshan were collected. The diffuse reflectance spectra were recorded on individual samples using an Antaris Ⅱ FT-NIR analyzer and the average spectral characteristics of different litter species were analyzed. During each modeling, 540 sample data were divided in to the training set and test set at a ratio of 2∶1 by using the Kennard-Stone algorithm. 360 sample data were used to develop discriminant models and 180 sample data were used to test the models. Single and combined spectral pretreatment methods (SNV, SG, MSC, and Derivative) were applied to improve the performance of discrimination models. Two qualitative pattern recognition methods (i. e., principal component analysis, PCA and orthogonal partial least-squares discrimination analysis, OPLS-DA) were conducted to identify the species of leaf litter. The results showed that: (1) the spectra data of different litter groups intertwined in the PCA score plot. Using SNV+SG as the pretreatment of spectra could improve the model parameter. PCA method cannot identify the leaf litter of six tree species, though Castanopsis wattii and Hartia sinensis can be separated from the rest litter species using the improved discriminant model. (2) SNV+SD pretreatment method combined with the OPLS-DA algorithm was used to develop discriminant models and showed excellent prediction ability (training set=100%; validation set=100%). Key statistical parameters of this model including R2Ycum and Q2Cum were 0.922 and 0.894, respectively. The permutation test indicated that the discriminant model was not overfitted. Our study indicated that NIR calibration models built with OPLS-DA algorithm have a good discriminative ability for different leaf litter species, and thus provide definite technological support for further plant litter research.