Objective An attention-based multiscale feature fusion network with intersected dual stream was proposed, namely MAFF-Net, for diffeomorphic brain image registration, in order to achieve rapid extraction and analysis of Alzheimer's disease-related brain structure labels. Methods The intersected dual stream network was used to infer the mutual mapping relationship between image pairs, then the multiscale feature information was fused by introducing the attention mechanism, finally diffeomorphic registration was introduced to enhance the continuity and global smoothness of the deformation field and improve the registration quality. Brain image registration experiments were conducted on self-collected, OASIS-AD, and OASIS-Health datasets. The performance of the MAFF-Net model was validated using metrics by Dice similarity coefficient (DSC), recall, average surface distance (ASD), and the Jacobian determinant. Further analysis was performed on the brain structure label extraction results from the OASIS dataset. Results The experimental results of brain image registration show that the MAFF-Net algorithm has DSC values of 0.832, 0.853, and 0.865 on the three test sets, negative Jacobian determinant voxel ratios of 0.027%, 0.192%, and 0.089%, Recall values of 0.924, 0.909, and 0.920, ASD values of 0.447mm, 0.387mm, and 0.345mm, with all but Recall being superior to the comparison algorithm. The results of brain structural label analysis on the OASIS dataset show that the volume and surface area of the cerebral cortex, hippocampus, and amygdala are closely related to age and health status. Conclusion The MAFF-Net model proposed in this paper can obtain accurate registration performance and label extraction results of brain MR Images, and provide auxiliary reference value for the early diagnosis of AD through the analysis of morphological characteristics of AD related brain structures.