Alzheimer’s disease (AD) is an irreversible neuro degenerative brain disease and the most common dementia in the elderly. Manual classification of Alzheimer
’s magnetic resonance image (MRI) has problems delay classification and time-consuming classification. As the aging population becomes more and more serious, accurately and quickly classify patients with AD has important research significance. This paper combines convolutional neural network (CNN) technology with MRI technology, and designs a 3D-ResNet model for AD classification, which achieves 98.39% accuracy, 96.74% sensitivity and 99.99% specificity on the validation set and achieves 97.43% accuracy, 94.92% sensitivity and 9999% specificity on the test set. The classification time of each patient is 0.23 s. In addition, for the problem that the pathogenesis of AD is not yet clear, this paper uses Class Activation Mapping (CAM) technology to visualize the AD-related brain regions.