With　the　rapid　development　of　deep　learning,　although　speech　conversion　had　made　great　progress,　there　are　still　rare　researches　in　deep　learning　to　model　on　singing　voice　conversion,　which　is　mainly　based　on　statistical　methods　at　present　and　can　only　achieve　one-to-one　conversion　with　parallel　training　datasets.　So　far,　its　application　is　limited.　This　paper　proposes　a　generative　adversarial　learning　model,　MSVC-GAN,　for　many-to-many　singing　voice　conversion　using　non-parallel　datasets.　First,　the　generator　of　our　model　is　concatenated　by　the　singer　label,　which　denotes　domain　constraint.　Furthermore,　the　model　integrates　self-attention　mechanism　to　capture　long-term　dependence　on　the　spectral　features.　Finally,　switchable　normalization　is　employed　to　stabilize　network　training.　Both　the　objective　and　subjective　evaluation　results　show　that　our　model　achieves　the　highest　similarity　and　naturalness　not　only　on　the　parallel　speech　dataset　but　also　on　the　non-parallel　singing　dataset.　©　2019　IEEE.