Multi-modality biomarkers were used for the classification of AD. Nonlinear graph fusion was used to investigate the multi-modal complementary information. Validations were performed in different classification scenarios. We achieved superior results than the state-of-the-art linear combination approaches. The proposed method provides an effective way to integrate multiple heterogeneous data for the classification of AD.