Typical　domain　adaptation　neural　network　that　takes　multisource　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　multisource　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　multisource　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　multidomain　adaptation　in　multisource　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%.