Alzheimer’s disease (AD) is a common and dangerous disorder that primarily impacts older adults, early detection is crucial, and diagnostic tools like PET and MRI have an impact on offering detailed anatomical and metabolic insights, respectively. However, traditional methods usually directly concatenate the two modal data by channel, which fails to fully utilize the complementary information provided by MRI and PET data. Hence, this paper introduces a new multi-modal dynamic information selection framework to enhance the accuracy of AD classification. It includes three main modules: a dual ResNet50-based feature extraction module; a modal fusion module containing a feature pyramid for finer detail extraction and a cross-attention mechanism to integrate modal information; a dynamic information selection module that evaluates the data content across modalities to optimize decision-making. The outcome on the ADNI dataset confirm the efficiency of the proposed approach.