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Objective To establish an early classification and prediction model of Alzheimer’s disease (AD) based on texture parameters of hippocampus of MR image.Methods The data were collected from ADNI database of National Institute on Aging, NIH.Magnetic resonance (MR) brain images were collected and extracted based on left, right and bilateral hippocampal images.Region growing algorithm and Contourlet transformation were used to extract texture features.Combined with the basic information of the research objects and texture features, Gaussian process classification method was used to establish a diagnosis model for AD patients and healthy control subjects, and a predictive model from mild cognitive impairment (MCI) into AD.The sensitivity, specificity and area under the ROC curve were evaluated.Results A total of 420 research objects were included in the study.The sensitivity, specificity and area under the ROC curve of the bilateral hippocampal images in the diagnosis model for AD patients and healthy control subjects were 92.7%, 87.1%, and 0.922, respectively, which were higher than those based on the left or right hippocampal area model.The sensitivity was 82.4% and the area under the ROC curve was 0.836 in AD early prediction model based on MCI data.Conclusions Contourlet texture of the hippocampus can be used to construct the predictive model to identify the early stage of AD, which helps to monitor the progression of MCI to AD, providing evidence for the prevention and treatment of AD.