This study proposes a novel approach named “hybrid high-order FC networks” to explore the higher-level interactions among brain regions for improving the diagnosis performance of early mild cognitive impairment. We first construct the low-order network and the topographical similarity-based high-order network. With the two-level FC networks, we propose to construct a new “associated high-order network”, which is formed by estimating the higher-level interactions between the high-order sub-networks and low-order sub-networks. We further devise a multi-kernel learning strategy to integrate the dynamic networks of the three different levels. A high diagnosis accuracy of 91.5 % demonstrates effectiveness of our proposed approach.