The quick and nondestructive diagnosis of the ramie leaves with the brown spot through the analysis of the hyperspectral information on leaves is of great significance to improving the production and the quality of ramie. Total 430 hyperspectral data，which are related to the ramie leaves suffering from the brown spot，and to the healthy ramie leaves， are collected by using a portable ASD spectrometer called FieldSpec 3 and a handheld leaf clip. From this，we present a subband principal components analysis (PCA) approach based on the coefficient of variation to extract the feature variables. Meanwhile，using respectively 1 to 10 main factors as the feature variables and together with the support vector classification(SVC) approach to build the identification model of the brown spot of ramie leaves. We get the following results：1) The four subbands，i.e. the band A(511~636 nm)， the band B(630714 nm)，the band C(1 406~1 511 nm) and the band D(1 870~2 450 nm)， have more higher coefficients of variation  than other bands and are the sensitive bands for building the identification model；2) The modeling effect of the band C is the best one of those 4 bands，and while using 5 to 10 PCA main factors as the feature variables to construct the SVC identification model，the accuracy rate is over 90% with the same number of main factors，which is apparently higher than that of the fullwave band and other subbands. Therefore，it is practicable to construct the SVC identification model on the brown spot of ramie leaves by performing PCA over the subbands that are more sensitive to the coefficient of variation and by appropriately choosing the number of main factors that act as the feature variables，which provides the technical support for developing a new diagnose of the brown spot of ramie.