Alzheimer's disease (AD) is a typical neurodegenerative disease that is associated with cognitive decline, memory loss, and functional disconnection. Diffusion tensor imaging (DTI) has been widely used to investigate the integrity and degeneration of white matter in AD. In this study, with one of the world's largest DTI biobanks (865 individuals), we aim to explore the diagnosis utility and stability of tractbased features (extracted by automated fiber quantification (AFQ) pipeline) in AD. First, we studied the clinical association of tract-based features by detecting AD-associated alterations of diffusion properties along fiber bundles. Then, a binary classification experiment between AD and normal controls was performed using tract-based diffusion properties as features and support vector machine (SVM) as a classifier with an independent site cross-validation strategy. The average accuracy of 77.90% (the highest was 88.89%) showed that white matter properties as biomarkers had a relatively stable role in the clinical diagnosis of AD.