Functional connectivity （FC） can represent the ability of brain regions to work together. At present， a combination of dynamic functional connectivity （DFC） and cluster analysis is widely used to study the significant difference analysis and classification of diseases. However， in the existing study， there is no clear standard for the determination of the number of clusters and the selection of clustering results， and the traditional DFC cannot represent the FC information of different frequencies. Therefore， this paper studies the clustering related factors of intrinsic frequency DFC in MRI signal of mild cognitive impairment （MCI）. First， the noise-assisted multivariate empirical mode decomposition of the time course （TC） data is performed and the DFC is calculated. Then， the cluster is analyzed through the evaluation-assisted clustering method， and the least square method is used to fit the clustering results. Finally， classifier is used for classification. The contribution of this paper is to suggest a more reasonable clustering method and a more number of clusters to obtain functional connections at different intrinsic frequencies. In the experiment， we used the Alzheimer’s disease neuroimaging （ANDI） database to test the proposed method. The experimental results show that the accuracy of supervised clustering used in this paper is higher than that of unsupervised clustering； the classification accuracy of DFC with natural frequency is higher than that of traditional DFC； the least square fitting can improve classification accuracy.