The　convergence　of　AI,　big　data,　and　magnetic　resonance　imaging　(MRI)　has　unlocked　transformative　potential　in　the　field　of　neurological　disease.　For　example,　the　resting-state　fMRI　technique　is　widely　applied　to　identify　functional　alterations　in　brain　and　perform　classification　tasks　for　Alzheimer＇s　Disease　(AD).　However,　the　high　network　complexity　and　difficult-to-interpret　features　of　these　models　make　it　challenging　to　translate　them　into　clinical　applications.　To　overcome　these　limitations,　the　systematic　Brain　Informatics　methodology　is　reconsidered　to　realize　evidence　combination　and　fusion　computing　from　resting-state　fMRI.　In　this　work,　a　NetFeatures-Transformer　is　proposed　to　perform　functional　brain　network　classification　through　the　integration　of　graph-based　evidence,　realizing　interpretable　discrimination　of　healthy　controls　(HC)　and　AD.　The　key　contributions　include:　combining　a　transformer　encoder　with　network　nodal　features　to　enhance　classification　performance,　capturing　different　aspects　of　the　brain　network＇s　topology.　Additionally,　the　model　utilizes　the　attention　scores　from　trained　transformer　encoders　to　enhance　interpretability,　revealing　the　significant　brain　connections　that　have　impact　on　the　classification　tasks　at　the　same　time.