Accurate diagnosis of Mild Cognitive Impairment (MCI) is critical to the prevention and treatment of Alzheimer's Disease (AD). Currently, deep learning and resting-state functional Magnetic Resonance Imaging (rs-fMRI) are often used to assist the diagnosis of MCI. The commonly used Pearson correlation method and Window Pearson (WP) correlation method can represent the brain Functional Connectivity (FC) in the time dimension, but cannot decompose and represent the information in different frequency dimensions. In order to solve this problem, a new method of using FC coefficients in different frequency dimensions as the input features of the existing deep learning was proposed to improve the accuracy of MCI classification. Firstly, the data of the subjects were spliced and then subjected to Multivariate Empirical Model Decomposition (MEMD). Secondly, the FC coefficients in different frequency dimensions were obtained after segmenting. Finally, VGG16 and Long Short-Term Memory (LSTM) network were used for testing. Experimental results show that, when the proposed FC coefficients ars used, the classification accuracy of MCI can reach up to 84.33%, which is 18.33-21.00 percentage points higher than the accuracy with the use of the traditional FC coefficients. In addition, the FC coefficients of different frequency dimensions have different resolutions for MCI.