Ramie（Boehmeiria nivea L）is a special and traditional fiber crop in China, having higher economic status. Determining the hyperspectral reflectance of ramie leaves with the spectrometer and developing a hyperspectrum-based method of ramie variety identification of high efficiency will be beneficial for the cultivation of ramie, the development and utilization of germplasm resources as well as the provision of critical technological supports to realize the top quality and high production of ramie and the accurate management of ramie croplands, which are significant for improving ramie yield and quality. In order to apply the hyperspectral technology for identifying ramie varieties, total 1458 hyperspectral data on the ramie leaves coming from nine ramie varieties of different genotypes were collected. According to these data, we explored the using of the Principal Components Analysis(PCA) to reduce dimensions of the hyperspectral data and how to determine the best appropriate number of principal factors in the PCA. Further, we compared different combinations constituted by different principal factors and different Discriminant Analysis approaches, and the results of the ramie variety identifying models based on the hyperspectrum of ramie leaves were established. After the principal component analysis of the full-band data sample, with 2~20 principal components as the feature variables, we applied three discriminant models, namely the Linear Discriminant analysis(LDA), the Quadratic Discriminant Analysis(QDA), and the Mahalanobis Distance Discriminant Analysis, (MD-DA), to create variety discriminant models and used them to predict, and with the accuracy of the prediction set as the evaluation criteria, the effects of various combinations were compared. The results showed that when we used the cumulative contribution rate(≥85%) as the criteria and selected two principal components, the accuracies for the LDA, the QDA and the MD-DA prediction sets were respectively 32.92%, 38.48% and 33.54%; but, when we used the feature value(≥1) as the criteria, and selected eleven principal components, the accuracies for the prediction sets of above discriminant models were respectively 68.72%, 87.04% and 83.54%; and further, when we considered the accuracy of the prediction set as the preferential criteria and selected twenty principal components, the accuracies for above discriminant models were all significantly improved and were respectively 84.98%, 95.68% and 95.27%. Therefore, we can draw the following conclusions: (1) it is feasible to establish the ramie leaf-based hyperspectral variety identification model by combining the PCA and the DA, but there are big differences between results due to different numbers of factors, different DA criterias and different combination approaches; （2）The impact of the number of principal factors on the identification results are significant, and the appropriate adding of the principal components can notably improve the accuracies of corresponding models, thus it is not confined to how to select the feature values of the PCA and the accumulative variance contribution rate ; (3) When the numbers of principal factors are the same, among above three discriminant criteria, the effect of the QDA is the best while that of the LDA is the worst; (4) Twenty principal components and the QDA approach constitute the best combination, which makes data dimensions be hugely reduced, from 2031 dimensions of the full-band down to 20 dimensions, and the accuracy of the prediction set is 95.68%.