Learning　brain　effective　connectivity　networks　(ECNs)　by　means　of　deep　learning　methods　from　functional　magnetic　resonance　imaging　(fMRI)　data　is　a　novel　study　hot　in　neuroinformatics　in　recent　years.　However,　current　methods　need　manually　tune　and　set　a　lot　of　model　hyper-parameters.　Once　the　parameter　setting　is　unreasonable,　it　will　seriously　restrict　the　performance　of　algorithms.　In　this　paper,　we　propose　a　novel　method　for　learning　ECNs　based　on　controllable　variational　autoencoder　(CVAE),　named　as　CVAEEC.　It　can　automatically　tune　model　parameters　and　learn　brain　effective　connectivity.　In　detail,　the　proposed　method　first　adopts　an　encoder　network　to　obtain　the　latent　variables　from　the　fMRI　data　of　brain　regions.　And　then,　based　on　the　latent　variables,　it　utilizes　a　decoder　network　to　obtain　the　generated　fMRI　data　of　brain　regions.　Once　the　generated　fMRI　data　is　highly　similar　to　real　fMRI　data　by　iteratively　training,　CVAEEC　algorithm　can　output　an　optimal　brain　ECN.　The　experimental　results　on　a　real　dataset　show　that　the　proposed　CVAEEC　is　able　to　better　learn　brain　ECN　compared　to　some　state-of-the-art　methods.　©　2021　IEEE.