Multi-modal neuroimaging technology provides crucial technical support for the early and precise diagnosis of Alzheimer’s disease （AD）. However， due to the inherent heterogeneity in imaging principles and feature representations across different neuroimaging modalities， the fusion of inter-modal information poses significant challenges. To address this issue， this study proposes a multi-modal fusion network （MFN） based on a 3D ResNet architecture for the early auxiliary diagnosis of AD. The proposed method first employs a 3D ResNet to separately extract feature representations from T1- and T2-weighted magnetic resonance images. Subsequently， an innovative cross-modal feature integration module （CFIM） is designed to overcome the limitations of direct concatenation. CFIM adopts a hierarchical fusion strategy， consisting of global information fusion module， local feature learning module and key factor module. Finally， the fused multimodal features are fed into a fully connected neural network for classification. Compared to early concatenation （fixed-weight fusion） and late fusion （shallow aggregation）， this strategy more effectively identifies disease-relevant diagnostic features. Experiments conducted on the Alzheimer’s disease neuroimaging initiative （ADNI） database demonstrate that the proposed method achieves higher accuracy and superior performance in AD classification tasks compared to existing approaches. Ablation studies further validate the effectiveness of each module， offering new technical insights for multi-modal neuroimaging analysis.