Abstract: Currently, the main approach of Alzheimer's Disease (AD) diagnosis is realized by structural magnetic resonance imaging (sMRI) , and the existing deep learning-based AD diagnosis is mainly based on 2D (converting 3D sMRI to 2D slices) or 3D convolutional neural networks, which cannot effectively capture 3D sMRI global features. To address this, this paper improves Swin Transformer to realize 3D block division for global features extraction and constructs a Transformer predictive classification model. Due to the sensitivity of existing Alzheimer's patients' regions with atrophy to the transformation of dimensions, the existing deep learning models are not capable of localizing the lesion regions. To overcome this, we propose an adaptive feature extraction module (AFEM) to realize the deformable adaptive feature extraction, and extend the basic 3D Transformer model to construct the AFEM-Transformer deep learning model to further enhance the feature learning ability of the model and realize adaptive localization of the specific location of the pathological region, which can be used to assist clinical diagnosis and realize the classification and prediction of Alzheimer's disease. In this study, sMRI images of 2248 subjects provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) were selected as the experimental dataset. The proposed AFEM-Transformer model for Alzheimer's disease diagnosis and mild cognitive impairment (MCI) progression prediction tasks will be evaluated and compared with existing convolutional neural network-based models and basic Transformer models. The results show that the experimental results of accuracy, sensitivity, specificity, and area under curve (AUC) value of the proposed AFEM-Transformer model on the two tasks show significant performance improvement compared to the convolutional neural network-based models and basic Transformer model, demonstrating the effectiveness of the proposed AFEM module. The proposed AFEM-Transformer deep learning model is able to accurately diagnose Alzheimer's disease and predict the progression of MCI, and can automatically localize the lesion area, which can be used as an effective computer-aided method in the clinical diagnosis of Alzheimer's disease.