The invention discloses an Alzheimer's disease early-stage prediction model based on cerebellar function connection characteristics. The prediction model comprises a patient information collection system, an AD disease screening system, a magnetic resonance data analysis and processing system, a biomarker event feature extraction system and a risk prediction and analysis system. On the basis of magnetic resonance data, multi-modal data of structural phase magnetic resonance, resting state functional magnetic resonance and arterial spin labeling perfusion magnetic resonance are combined, and outcome and prognosis conditions of patients with different cognitive function states are predicted by using a feature classification method. And clinical doctors can select more effective treatment means. The magnetic resonance detection method combination can play a synergistic role, the evaluation efficiency of a single detection method is improved, and outcome and prognosis of a patient with cognitive impairment are effectively predicted. Compared with an existing method, the optimized combination of the method is more efficient, and the limitation that the existing method is high in cost, long in time and large in wound is reduced.