As　a　novel　deep　learning　method,　deep　forest　has　achieved　excellent　classification　performance　on　many　small-scale　datasets,　thus　providing　a　new　opportunity　to　accurately　classify　brain　networks　(BNs)　on　limited　fMRI　data.　Though　there　are　a　few　explorations　about　classifying　BNs　using　deep　forest,　they　only　adopt　sliding　windows　to　extract　adjacent　features　of　BNs　and　fail　to　use　prior　knowledge　to　strengthen　the　features　more　relevant　to　brain　diseases.　In　this　paper,　we　propose　a　deep　forest　framework　with　multi-channel　message　passing　and　neighborhood　aggregation　mechanisms　(DF-MCMPNA)　to　extract　and　aggregate　long-range　multi-channel　topological　features.　Firstly,　we　use　the　three　intrinsic　connectivity　networks　(ICNs)　and　the　whole-brain　to　form　four　feature　extraction　channels.　Secondly,　we　present　a　multi-channel　message　passing　mechanism　and　a　channel-shared　neighborhood　aggregation　mechanism　to　recursively　extract　long-range　multi-channel　topological　features,　where　the　first　mechanism　can　learn　local　topological　features　in　each　channel　and　the　second　mechanism　can　fuse　multi-channel　topological　features.　Finally,　the　extracted　features　are　fed　into　the　casForst　to　perform　further　feature　learning　and　classification.　Experimental　results　on　ABIDE　I,　ABIDE　II,　and　ADHD-200　datasets　show　that　the　DF-MCMPNA　outperforms　several　state-of-the-art　methods　on　classification　performance　and　accurately　identifies　abnormal　brain　regions.