Typical　domain　adaptation　neural　network　that　takes　multi-source　heterogeneous　data　as　input　usually　achieves　poor　diagnostic　accuracy　in　induction　motor　fault　diagnosis　under　cross-operating　conditions.　Aiming　at　this　problem,　the　present　study　proposes　an　adversarial　multi-source　data　subdomain　adaptation　(AMDSA)　model.　This　model　encapsulates　three　types　of　modules:　a　shared　feature　extractor,　a　label　predictor　and　a　series　of　domain　discriminators.　The　joint　operation　of　the　shared　feature　extractor　and　the　domain　discriminators　is　used　to　perform　subdomain　adaptation　of　different　types　of　data　for　obtaining　domain　invariant　features　of　multi-source　heterogeneous　data.　The　label　predictor　is　employed　to　fuse　these　domain　invariant　features　and　realize　label　classification.　The　proposed　model　can　solve　the　problem　of　multi　domain　adaptation　in　multi-source　heterogeneous　data　through　constructing　a　subdomain　adaptation　strategy　and　a　feature　fusion　strategy.　The　effectiveness　of　AMDSA　is　verified　by　a　series　of　diagnostic　experiments　on　faulty　induction　motors　under　cross-operating　conditions.　The　experimental　results　show　that　the　average　diagnostic　accuracy　of　all　cross-operating　conditions　reaches　97.62%.　IEEE