A　main　goal　of　heterogeneous　transfer　learning　algorithms　is　to　solve　the　domain　adaptation　problem　of　different　feature　spaces.　However,　some　existing　heterogeneous　transfer　learning　methods　usually　only　extract　common　features　from　the　source　domain　and　target　domain,　ignoring　specific　features,　which　may　damage　the　performance　of　transfer　learning.　Therefore,　a　hierarchical　filter　transfer　learning　algorithm　(HFTLA)　for　heterogeneous　domains　in　is　proposed.　First,　a　nonlinear　mapping　is　constructed　to　learn　the　potential　relationship　between　the　features　of　different　domains.　Then,　the　feature　space　can　be　aligned　by　learning　common　features　and　specific　features,　which　can　ensure　the　integrity　of　the　features.　Second,　a　hierarchical　filter　framework　is　developed　to　play　different　roles　in　different　stages　of　transfer　learning.　In　the　pretransfer　phase,　a　knowledge　filter　based　on　genetic　principle　is　designed　to　increase　the　diversity　of　knowledge　with　different　genetic　operators.　In　the　post-transfer　phase,　a　guided　filter　is　established　to　achieve　a　coupling　balance　between　source　knowledge　and　target　information.　Finally,　experimental　results　on　heterogeneous　domains　illustrate　the　effectiveness　of　HFTLA.　©　2023　IEEE.