Alzheimer’s disease (AD) is a chronic neurodegenerative disease that could cause severe cognitive damage to the patients. Diagnosis of AD at its preclinical stage, i.e., mild cognitive impairment (MCI), could help to prevent or slow down AD progression. With machine learning, automatic MCI diagnosis could be achieved. Most of the previous studies mainly share a similar frame-work, i.e., building a classifier based on the features extracted from static or dynamic functional connectivity. Recently, inspired by the great successes achieved by deep learning in other areas of medical image analysis, researchers have introduced neural network models for MCI diagnosis. In this paper, we propose dynamic routing capsule networks for MCI diagnosis. Our proposed methods are based on a novel neural network fashion of capsule net. Two variants of capsule net are designed and discussed, which respectively uses the intra-ROIs and inter-ROIs dynamic routing to obtain functional representation. More importantly, we design a learnable dynamic functional connectivity metric in our inter-ROIs dynamic model, in which the functional connectivity is dynamically learned during network training. To the best of our knowledge, it’s the first time to propose dynamic routing capsule networks for MCI diagnosis. Compared with other machine learning methods and deep learning model, our method can achieve superior performance from various aspects of evaluations.