磁共振成像和正电子发射断层扫描是阿尔茨海默病早期诊断常用的成像技术．结合这两种模态可同时利用解剖和代谢信息更全面地评估大脑状态．然而，传统多模态融合主要通过通道拼接，未能充分利用模态间的互补信息，影响了模型的有效性．为此，本文提出了一种基于对抗学习与交叉注意力的多任务阿尔茨海默病分类模型．该模型通过对抗学习减少不同模态间的特征差异，并用交叉注意力进行特征融合，同时将脑龄预测任务作为辅助任务提升分类性能．实验结果表明，该方法在阿尔茨海默病、轻度认知障碍和正常对照分类任务中，准确率和F1分数分别达到91.10%和91.01%，这不仅提高了早期诊断的准确性，还增强了对疾病进展的监测能力，为阿尔茨海默病的临床干预提供重要支持．

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are commonly used imaging techniques for the early diagnosis of Alzheimer's disease (AD). The combination of these two modalities enables a more comprehensive assessment of brain status by utilizing both anatomical and metabolic information. However, traditional multimodal fusion, which relies primarily on simple channel splicing, fails to fully exploit the complementary information across modalities and limits the model's effectiveness. To address this, this paper proposes a multi-task classification model for AD based on adversarial learning and cross-attention. The model reduces inter-modal feature discrepancies through adversarial learning, followed by feature fusion via cross-attention, and introduces a brain age prediction task as an auxiliary task to improve classification performance. Experimental results demonstrate that the proposed method achieves an accuracy of 91.10% and an F1 score of 91.01% in classifying AD, mild cognitive impairment (MCI), and normal controls (NC). This not only enhances the accuracy of early diagnosis but also strengthens the ability to monitor disease progression, thereby providing strong support for clinical interventions in AD.