An aging population will inevitably make a higher prevalence of Alzheimer′s disease (AD), which results in a heavy burden on family and society. The early detection is the key to delaying or reversing the course of the disease. However, the current detection methods cannot meet the needs of inexpensive, low-invasive, rapid and reliable diagnosis of AD. The detection technology based on the biofluid spectra shows great potential in medical diagnosis. However, the challenge in the detection of dementia is difficult in extracting the characteristic information related to dementia in the plasma spectra and the complicated classification problem of multiple disease courses. For this reason, the research work will be carried out from the perspective of information space construction, feature information mining, and detection system design. An adaptive screening and diagnostic model for multiple disease courses of AD driven by the model feature wavenumber is constructed. For three different stages of AD, including early, middle, and late stages, the sensitivity of detection is 90. 0% , 87. 5% , and 100% , respectively. And the specificities are 83. 3% , 93. 7% , and 100% , respectively. Experimental results show that the proposed detection model provides superior classification power for AD.