Abstract: For screening out an index of peanut (Arachis hypogaea) drought resistance and breeding new varieties of peanut that has high drought resistance capability, conventional linear selection method can not reflect nonlinear characteristics of drought resistance on peanut and the index conformity degree screened by it is limited. In this study, a kind of nonlinear feature selection model based on support vector machine has been set up, characterization factors were assessed individually by SVM cross validation, characterization factors which had adverse effects on model were eliminated by the minimum MSE. Taking 29 peanut varieties as examples, 5 characteristic indexes, including length of main stem, No. of branches, shoot dry weight, leaf dry weight and specific leaf area, were obtained by analyzing and screening 13 configuration features and 13 physiological features in the seedling stage. Leave-one-out accuracy of nonlinear and linear feature selection model based on these characteristic indexes is higher than that of linear feature selection model. The experimental results showed the accuracy of characterization factors screened by nonlinear feature selection. Finally, test of SVM regression significance analysis based on F-test and analysis of one-factor importance validated the importance of indexes reserved. Nonlinear prediction model based on these indexes reserved could provide effective guidance for drought resistance and breeding of peanut.