As a noninvasive brain imaging method, structural Magnetic Resonance Imaging(sMRI) plays an important role in understanding the pathology of Alzheimer’s Disease(AD). Convergence evidence suggests that imaging features based on sMRI performs well in classifying AD patients from Normal Controls(NC). In this paper, a simple feature extraction method is proposed based on the Human Brainnetome Atlas with 227 AD patients and 226 NC from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Firstly, mean gray matter density of each brain region in the brainnetome atlas is obtained as feature. After that, an Support Vector Machine(SVM) model is introduced to classify AD from NC samples. The result shows that the mean accuracy is 85.2% with 10-fold cross validation. And post hoc analysis demonstrates that the mean gray matter density of several brain regions such as the hippocampus, amygdala and fusiform gyrus play important roles in classification and the atrophy of these regions has significant correlation with cognitive ability measured by the Mini-Mental State Examination(MMSE) scores in AD patients. In addition, the Least Absolute Shrinkage and Selection Operator(LASSO) is utilized to predict individual MMSE score and the predicted values show a very high positive correlation with true values (r0.65, p0.001). In conclusion, the present study demonstrates that the gray matter density of brain regions defined by the Brainnetome Atlas is meaningful for distinguishing AD patients from NC samples and could also be utilized to predict individual cognitive ability.