Using visible-near infrared spectra to classify different species of exopalaemon was studied. Successive projections algorithm (SPA) combined with uninformative variable elimination (UVE) were used to select effective wavelengths from visible and near infrared (Vis-NIR) bands. The selected effective wavelengths were set as inputs of least square-support vector machine (LS-SVM) for the classification of three typical exopalaemon species, namely, E. carincauda, E. modestus and E. orientis. Kennard-Stone algorithm was used to select 150 samples for calibration and the remaining 50 samples for prediction. Twelve effective wavelengths were selected by UVE-SPA, and they were 392,431,517, 551,595, 627, 676, 734, 760, 861, 943 and 1018 nm. The correct rate is 92.00% for classifying samples in prediction set by LS-SVM model based on these twelve effective wavelengths. The overall results demonstrate that it is feasible to utilize Vis-NIR spectroscopy to classify different species of exopalaemon, and UVE-SPA can extract the most effective wavelengths to build the LS-SVM model with an optimal classification result.