The plasma samples of Alzheimer disease（AD） patients are collected using Fourier transform infrared-attenuated total reflection （FTIR-ATR） spectroscopy technology. Based on the FTIR-ATR spectral data of the plasma membrane samples， the spectral data are encoded into two-dimensional images by utilizing the Gram angular field （GAF） and Markov transition field （MTF）. Meanwhile， a neural network model based on the deep residual networks and attention mechanism is combined to conduct the screening and classification research on Alzheimer disease. The experimental results show that the GAF-MTF-CNN model can effectively improve the accuracy of spectral feature extraction. Additionally， the method of combining two-dimensional data with deep learning has better classification accuracy compared with traditional classification methods. Encoding spectrum into images using GAF and MTF techniques， and combining them with an improved residual neural network， effectively enhances the generalization ability and diagnostic accuracy of AD screening models， optimizing the screening performance.
