Mild cognitive impairment(MCI)as the potential sign of serious cognitive decline could be divided into two stages,i.e.,late MCI(LMCI)and early MCI(EMCI).Al-though the different cognitive states in the MCI progression have been clinically defined,effective and accurate identifica-tion of differences in neuroimaging data between these stages still needs to be further studied.In this paper,a new method of clustering-evolutionary weighted support vector machine ensemble(CEWSVME)is presented to investigate the alter-ations from cognitively normal(CN)to EMCI to LMCI.The CEWSVME mainly includes two steps.The first step is to build multiple SVM classifiers by randomly selecting samples and features.The second step is to introduce the idea of cluster-ing evolution to eliminate inefficient and highly similar SVMs,thereby improving the final classification performances.Addi-tionally,we extracted the optimal features to detect the differen-tial brain regions in MCI progression,and confirmed that these differential brain regions changed dynamically with the devel-opment of MCI.More exactly,this study found that some brain regions only have durative effects on MCI progression,such as parahippocampal gyrus,posterior cingulate gyrus and amyg-dala,while the superior temporal gyrus and the middle temporal gyrus have periodic effects on the progression.Our work con-tributes to understanding the pathogenesis of MCI and provide the guidance for its timely diagnosis.