Alzheimer's disease (AD) is a neurodegenerative disorder that severely impairs patients’ cognitive functions and daily living abilities. EEG signals possess unique advantages in the early diagnosis of cognitive dysfunction. This study aims to explore an effective AD diagnostic method using EEG signals and deep learning techniques. We conducted signal analysis and image processing on EEG data from 65 subjects, including 36 AD patients and 29 healthy controls. Image feature extraction was performed using a vision transformer and a masked autoencoder, and a feature-level fusion strategy was employed to construct multidimensional EEG features. The study found that using a multilayer perceptron for AD detection achieved an accuracy of 96.92%, a sensitivity of 97.22%, and a specificity of 96.55%. The results validate the effectiveness of single-modal multidimensional EEG features in AD diagnosis and provide a scientific basis for the application of deep learning in the early diagnosis of AD.