For airport noise prediction, aiming at the high cost and large error of contour drawing, as well as the lack of guidance standard in regression method based on SVM classification, it presents a method of cluster regression based on support vector machine (SVM). Cluster regression in prediction of airport noise, using k-means algorithm, takes advantage of the characteristics of clustering. It firstly limits the sample within the same class, and then performs regression in the similar class. Experimental results on housing data set and Laser generated data set show that the fitted values of the cluster regression method are more accurate than the direct regression method. Applied the method to measured data of an airport in Beijing, and compared it with other prediction models, the accuracy of cluster regression is superior to that of other prediction methods.