Sometimes　there　could　be　sufficient　data　in　used　aircraft　fuel　pump.　A　few　data　in　a　specific　new　pump,　which　low　accuracy　of　diagnosis,　are　obtained.　In　this　paper,　a　fault　diagnosis　method　of　centrifugal　aircraft　fuel　pump　based　on　migration　learning　is　proposed.　The　features　from　the　fault　data　of　similar　pumps,　which　are　used　as　auxiliary　data　set,　are　extracted.　Then,　the　training　data　set,　which　is　composed　of　auxiliary　data　and　a　few　amount　of　diagnostic　target　data,　is　established　and　is　trained　by　TrAdaboost　transfer　learning　algorithm.　Finally,　compared　with　traditional　machine　learning,　it　shows　that　the　transfer　learning　has　obvious　diagnostic　advantages　in　the　situation　of　insufficient　diagnostic　target　data.