Current　studies　have　shown　that　the　classification　of　human　brain　functional　networks　(BFN)　is　a　reliable　way　to　diagnose　and　predict　brain　diseases.　However,　a　great　challenge　for　current　traditional　machine　learning　methods　and　deep　learning　methods　is　their　poor　performance　or　lack　of　interpretability.　To　alleviate　this　problem,　we　propose　a　novel　causal　learning　and　knowledge　fusion　mechanism　for　brain　functional　network　classification,　named　CLKF.　The　proposed　mechanism　first　extracts　causal　relationships　among　brain　regions　from　functional　magnetic　resonance　imaging　(fMRI)　data　using　partial　correlation　and　conditional　mutual　information,　and　obtains　the　relationships　between　BFN　and　labels　by　Gaussian　kernel　density　estimation.　Then,　it　fuses　these　two　types　of　relationships　as　knowledge　to　aid　in　the　classification　of　brain　functional　networks.　The　experimental　results　on　the　simulated　resting-state　fMRI　dataset　show　that　the　proposed　mechanism　can　effectively　learn　the　causal　relationships　among　brain　regions.　The　results　on　the　real　resting-state　fMRI　dataset　demonstrate　that　our　mechanism　can　not　only　improve　the　classification　performance　of　both　traditional　machine　learning　and　deep　learning　methods　but　also　provide　an　interpretation　of　the　results　obtained　by　deep　learning　methods.　These　findings　suggest　that　the　proposed　mechanism　has　good　potential　in　practical　medical　applications.