A method to predict the change trend and space of aeroengine parameters with fuzzy information granulation (FIG) and optimized support vector machine (SVM) was put forward. FIG was adopted to granulate the parameters. Genetic algorithm (GA) was applied into adaptive selection of the best penalty parameter and kernel function parameter with Kfold cross validation (KCV) error minimum as the optimization goal. The SVM model was trained for nonlinear prediction of fuzzy particles. The verification results of some airlines monitoring performance parameters data of an aeroengine showed that the algorithm proposed can effectively realize the change trend and spatial prediction of aeroengine performance parameters. In addition

the influence of window size on prediction accuracy and the effect of multistep prediction were studied on the basis of instance. As a result

it was concluded that the best window size was three data and the forecasting error within three steps was less than 10%.