Screening indexes of drought resistance in crops is a puzzler with a few samples, multi-index and non-linear characteristics. Rationality of linear regression model and the indexes obtained by linear screening based on empirical risk minimization are debated. On the contrary, support vector machine based on structural risk minimization has the advantages of non-linear characteristics, fitting for a few samples, avoiding the over-fit, strong generalization ability and high prediction precision, etc. In this paper, setting the survival percentage under repeated drought condition as the target and support vector regression as the nonlinear screen tool, six integrated indicators including plant height, proline, malondialdehyde, leaf age, area of the first leaf under the central leaf and ascorbic acid, were highlighted from 24 morphological and physiological indicators in 15 paddy rice cultivars. The results showed that support vector regression model with the six integrated indicators had a more distinct improvementin fitting and prediction precision than the linear reference models. Considering the simplicity of indicators measurement, the support vector regression model with the only six morphological indicators including shoot dry weight, area of the second leaf under the central leaf, root shoot ratio, leaf age, leaf fresh weight and area of the first leaf under the central leaf was also feasible. Furthermore, an explanatory system including the significance of regression model and the importance of single indicator was established based on support vector regression and F-test.