A novel variable selection method based on stability competitive adaptive reweighted sampling was applied to work with support vector machines（SVM-SCARS） for selecting informative variables of near infrared spectroscopy to build more robust SVM model.This method computed the stability index of each variable from a statistical analysis of weight vectors of multiple SVMs trained on subsamples of the original data by multiple sampling.The stability index represents the influence of variable on SVM modeling and could be used to evaluate the importance of variable.The variable with higher stability index was treated as informative variable that has an important effect on predictive performance of the model.Through iterations,the important variables was selected gradually by using adaptive reweighted sampling technology.Then the selected variables in each iteration were stored into variable subset.The optimal variable subset was determined by assessing the correct classification rate of cross validation（CCRCV） of SVM models based on all variable subsets.The SVM-SCARS algorithm combined with near-infrared diffusion reflectance spectrum technology were applied to construct wood identification model for four kinds of eucalyptus and two kinds of acacia.Experimental results showed that the SVM-SCARS model has a superior performance for identifying different wood species,in comparison to the full spectrum model and the support vector machine recursive feature elimination（SVM-RFE） model,both in terms of prediction ability and selected variables size.As a result,fifteen variables were selected by SVM-SCARS method to construct identification model with the correct classification rate of 97.9%.This study demonstrates that SVM-SCARS could effectively extract important characteristic variables from near infrared spectrum to improve the robustness and applicability of NIR online detection model for wood property analysis.