The　integration　of　AI,　big　data,　and　magnetic　resonance　imaging　(MRI)　has　significantly　advanced　healthcare,　particularly　in　the　early　diagnosis　and　treatment　of　neurodegenerative　diseases.　Alzheimer’s　Disease　(AD),　which　predominantly　affects　the　elderly,　and　its　precursor,　Mild　Cognitive　Impairment　(MCI),　present　considerable　challenges　in　early　detection　due　to　the　complex　structural　changes　in　the　brain.　Traditional　diagnostic　methods　often　struggle　to　capture　these　intricate　changes.　To　address　this,　the　systematic　Brain　Informatics　methodology　is　reconsidered　to　realize　evidence　combination　based　on　dual-structural　representation　and　fusion　computing　via　attention-based　graph.　In　particular,　the　DS-AGF　(Dual-Structural　Representation　Learning　with　Attention-Based　Graph　Fusion)　model　was　proposed　for　hierarchical　MCI-AD　diagnosis.　This　model　employs　a　dense　convolution　unit　and　a　node-optimized　convolution　unit　to　learn　multi-level,　fine-grained　brain　representations.　Additionally,　an　attention-based　graph　representation　fusion　unit　is　introduced,　enabling　the　integration　of　these　representations　and　allowing　the　model　to　capture　both　local　and　global　relationships　between　brain　regions.　This　approach　enhances　the　network　feature　learning　ability　on　critical　features,　enabling　multi-view　visualization　of　key　brain　regions　and　their　connectivity.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.