Objective  To explore the application of artificial intelligence (AI) techniques in the automatic identification of neuropathological features of Alzheimer’s disease (AD) for potential teaching scenario.  Methods  A total of 34 brain tissue samples were included, comprising controls and Alzheimer’s disease (AD)-like lesions across various pathological stages (early, middle, and late). To characterize the hallmark features of the disease, high-resolution neuropathological images were acquired using three specific staining protocols, methenamine silver (M-Ag) for β-amyloid deposition, phosphorylated tau (p-tau) immunohistochemistry for neurofibrillary tangles, and Gallyas silver staining for neuritic plaques. AI models were primarily constructed using platforms such as Visiopharm to achieve automated segmentation and identification of the three pathological features. The performance of the AI models was validated against manual annotations by expert neuropathologists, which served as the “gold standard”. The intersection over union metric was employed to evaluate the accuracy of the algorithms in feature segmentation and quantitative analysis.  Results  Comparison with manual annotations by neuropathology experts showed that the U-Net algorithm in Visiopharm software achieved an identification accuracy of up to 86%, which was largely consistent with expert annotations.   Conclusion  AI technology can partially accurately identify AD pathological features and holds the potential for transformation into teaching tools. The findings provide a practical foundation for developing AI-enhanced intelligent teaching models in neuropathology education.