As the prodromal stage of Alzheimer's disease, effective recognition of mild cognitive impairment can reduce the prevalence of Alzheimer's disease. At present, most of the research on the recognition of mild cognitive impairment is carried out through biomarkers and neuroimaging, which is not conducive to large-scale analysis and research. Based on neuropsychological evaluation and life habits questionnaires, this article applies machine learning methods to the recognition of mild cognitive impairment and conducts experimental research. A questionnaire survey of the elderly was conducted to obtain raw data, including demographic variables, daily habits and neuropsychological data. Feature selection is carried out through filter screening method and the influence of factors such as lifestyle, physical health and learning ability on the morbidity of mild cognitive impairment is analyzed. The classifier mainly uses three methods: artificial neural network, support vector machine and random forest. The experimental results show that the random forest classification effect is the best and the accuracy rate is as high as 92%. Artificial neural network has strong generalization ability with 91% accuracy rate. Support vector machine has inferior effect.