Alzheimer’s disease （AD） is an irreversible neurodegenerative disease， whose early medical intervention is of great significance to control and improve the condition. In recent years， deep learning methods have been widely used by researchers to analyze magnetic resonance imaging （MRI） of AD for early diagnosis. However， the changes of brain structure are less different from those of normal people in the early stage， and the existing single-scale analysis methods are difficult to capture these subtle differences. Aiming at the above problem， this paper proposes a feature enhanced pyramid network （FEPN） for early diagnosis of AD. The high-level features are supplemented by the contextual information extracted from the designed shallow feature re-extraction， and the fusion weights are calculated to guide the fusion of high-level and low-level feature maps， which enhance the interaction of contextual information and the matching degree of multi-scale feature fusion. The Alzheimer datasets published by Kaggle are employed to conduct comparison experiments to verify the performance of the proposed approach. The comparison experiment employs the Alzheimer dataset published by Kaggle to verify the performance. Compared with related methods， FEPN achieves the SOTA classification accuracy of MRI of four AD brain states （non-demented， very mild demented， mild demented， moderate demented）.