The use of resting-state functional magnetic resonance imaging technology to obtain functional connection （FC） of brain regions is widely used in classification studies of mild cognitive impairment （MCI）. However， the classification of whole-brain FC usually has the problems of information redundancy and feature dimension disaster. Therefore， a new method of “G-Lasso + feature compression” is proposed to solve the above problems. Firstly， the blind source separation technology is used to obtain the active signal time series of the whole brain functional brain region， and the FC sparse network is constructed by G-Lasso. Secondly， the sparse FC of MCI， normal subjects and all subjects on the group average is calculated， and the cluster Class 1—Class 3 center decision is performed in combination with the Euclidean distance to obtain the difference feature information between clusters. Finally， the sparse FC of each participant is expressed as a linear combination of the cluster center， and the compressed FC is obtained as the key feature to complete the classification. The results show that the proposed method obtains significant differences in inter-cluster features after Class decision and provides effective sign information. The classification accuracy of the key features obtained by further compressing （89.8%） is 5%—10% higher than that of the sparse method alone. The results show that in order to solve the problems of whole-brain FC， feature selection and dimensionality reduction need to be considered， but there are many uncertain factors， and “sparse + compression” can be appropriately combined.