MCI (Mild Cognitive Impairment) is a pre-stage of Alzheimer’s Disease (AD). And the early detection of MCI is important for early treatment of AD patients. In this work, prediction efficiency of early stage of AD based on the functional connectivity network characteristics was evaluated by using a couple of classifiers with AAL_90 and AAL_1024 templates. The results showed that brain functional characteristics were effective in the prediction of MCI with a SVM-based classifier. And a more fine template could improve prediction accuracy.