According as the main factor deciding the performance of ensemble learning is the diversity of component learners, clustering technology is used to speed up AdaBoost in this paper. The performance of the new algorithm is very close to the AdaBoost in learning deferent noise levels data sets. The new algorithm can be used to detect and eliminate noisy data quickly, and could achieve rapid learning on data sets after eliminating noise. It overcomes the noise-sensitive shortcoming of AdaBoost. The general performance and efficiency of the new algorithm are much better than AdaBoost in processing data sets containing noise.