Brain　disease　diagnosis　is　a　new　hotspot　in　the　cross　research　of　artificial　intelligence　and　neuroscience.　Quantitative　analysis　of　functional　magnetic　resonance　imaging　(fMRI)　data　can　provide　valuable　biomarkers　that　contributes　to　clinical　diagnosis,　and　the　analysis　of　functional　connectivity　(FC)　has　become　the　primary　method.　However,　previous　studies　mainly　focus　on　brain　disease　classification　based　on　the　low-order　FC　features,　ignoring　the　potential　role　of　high-order　functional　relationships　among　brain　regions.　To　solve　this　problem,　this　study　proposed　a　novel　multi-level　FC　fusion　classification　framework　(MFC)　for　brain　disease　diagnosis.　We　firstly　designed　a　deep　neural　network　(DNN)　model　to　extract　and　learn　abstract　feature　representations　for　the　constructed　low-order　and　high-order　FC　patterns.　Both　unsupervised　and　supervised　learning　steps　were　performed　during　the　DNN　model　training,　and　the　prototype　learning　was　introduced　in　the　supervised　fine-tuning　to　improve　the　intra-class　compactness　and　inter-class　separability　of　the　feature　representation.　Then,　we　combined　the　learned　multi-level　abstract　FC　features　and　trained　an　ensemble　classifier　with　a　hierarchical　stacking　learning　strategy　for　the　brain　disease　classification.　Systematic　experiments　were　conducted　on　two　real　large-scale　fMRI　datasets.　Results　showed　that　the　proposed　MFC　model　obtained　robust　classification　performance　for　different　preprocessing　pipelines,　different　brain　parcellations,　and　different　cross-validation　schemes,　suggesting　the　effectiveness　and　generality　of　the　proposed　MFC　model.　Overall,　this　study　provides　a　promising　solution　to　combine　the　informative　low-order　and　high-order　FC　patterns　to　further　promote　the　classification　of　brain　diseases.