Focused on the issue that interference would exist in the analysis and prediction of airport energy data because of the outliers, a prediction model based on improved Fuzzy Support Vector Regression (FSVR) was established for the demand of airport energy. Firstly, a fuzzy statistical method was selected to make an analysis on test sample sets, parameters and the outputs of models, and a basic membership function form consistent with the data distribution would be derived from this analysis. Secondly, relearning of membership function would be performed with respect to expert experiences, then the parameter values a and b of the normal membership function, the boundary parameter values of semi-trapezoid membership function and the parameter values p and d of triangular membership function would gradually be refined and improved, so as to eliminate or reduce the outliers which were not conducive to data mining and reserved the key points. Finally, combined with Support Vector Regression (SVR) algorithm, a prediction model was established and its feasibility was verified subsequently. The experimental result shows that, compared with Back Propagation (BP) neural network, the prediction accuracy of the FSVR increases 2.66% and the recognition rate of outliers increases 3.72%.