To select proper parameters for the support vector machine (SVM) regression model used for the prediction of nonlinear structural response, the particle swarm optimizer was introduced into parameter optimization. To make comparisons, the SVM regression model with regular parameters, the RSM and RBFNN regression models were also developed based on the  training data set. The results show that the SVM regression model based on optimized parameters has a better prediction ability than the regular SVM and can solve the overfitting problem in the regression model developed by the response surface method and radial based function neural network, thus possessing better generalization ability. The application of the SVM with optimimized parameters in structural optimizations proves that it has good engineering practicability.