Identifying　brain　effective　connectivity　networks　from　functional　magnetic　resonance　imaging　(fMRI)　data　is　an　important　advanced　subject　in　neuroinformatics　in　recent　years,　where　the　learning　method　based　on　bayesian　networks　(BN)　has　become　a　new　hot　topic　in　the　field.　This　paper　proposes　a　new　method　to　learn　the　brain　effective　connectivity　network　structure　by　combining　ant　colony　optimization　(ACO)　with　BN　method,　named　as　ACOEC.　In　the　proposed　algorithm,　a　brain　effective　connectivity　network　is　first　mapped　onto　an　ant,　and　then　the　ant　colony　optimization　by　simulating　real　ants　looking　for　food　is　employed　to　construct　network　structures　and　finally　an　ant　with　the　highest　score　is　obtained　as　the　optimal　solution.　The　experimental　results　on　simulated　and　real　fMRI　data　sets　show　that　the　new　method　can　not　only　accurately　identify　the　connections　and　directions　of　the　brain　networks,　but　also　quantitatively　describe　the　connection　strength　of　the　brain　networks,　which　has　a　good　clinical　application　prospects.