Learning　brain　effective　connectivity　(EC)　networks　is　an　important　topic　within　the　community　of　human　brain　connectome.　It　is　of　great　significance　for　the　early　diagnosis　and　pathological　study　of　brain　diseases　to　accurately　identify　the　brain　EC　network　structure.　This　paper　combines　the　Firefly　Algorithm　(FA)　with　Bayesian　network,　and　proposes　a　new　method　to　learn　brain　EC　networks　by　FA　with　a　reproductive　mechanism.　The　new　method　uses　K2　score　as　the　evaluation　method　of　absolute　brightness　of　fireflies,　uses　the　optimization　of　firefly　population　to　complete　the　learning　of　brain　EC　networks,　and　uses　reproductive　mechanism　to　further　optimize　the　population.　First,　a　firefly　individual　represented　a　brain　EC　network　with　a　few　edges,　which　was　gradually　constructed　through　the　directional　movements　and　random　movements　of　the　firefly　individual.　Then,　a　reproductive　mechanism　was　employed　to　optimize　the　quality　of　networks　after　a　certain　number　of　evolution　iterations.　Finally,　the　network　structure　represented　by　the　individuals　with　the　highest　absolute　brightness　in　the　population　was　used　as　the　learning　brain　EC　network.　Experimental　results　on　many　simulated　datasets　verified　the　effectiveness　of　the　reproductive　mechanism,　and　the　new　algorithm　has　obvious　advantages　on　the　whole　performance　compared　with　other　algorithms.　Experimental　results　on　real　datasets　also　show　the　potential　practicability　of　the　new　algorithm.　©　2019,　Editorial　Board　of　Journal　of　Harbin　Institute　of　Technology.　All　right　reserved.