The　working　environment　of　seawater　axial　piston　hydraulic　pump　is　harsh,　and　it　is　difficult　to　diagnose　due　to　insufficient　fault　database.　In　contrast,　pumps　of　the　same　type　but　using　hydraulic　oil　have　an　adequate　fault　database　and　are　easy　to　diagnose.　In　view　of　the　above　situation,　a　fault　diagnosis　method　of　seawater　hydraulic　piston　pump　based　on　transfer　learning　is　proposed.　The　method　decomposes　the　original　sampled　fault　signal　by　complementary　ensemble　empirical　mode　decomposition　(CEEMD)　to　obtain　the　intrinsic　mode　function　(IMF)　that　can　characterize　the　original　signal.　The　singular　value　decomposition　(SVD)　is　performed　on　the　IMF.　Then,　the　obtained　singular　value　is　used　as　a　feature　parameter　to　construct　a　feature　vector.　The　feature　data　of　seawater　hydraulic　pump　and　oil　pump　are　used　as　target　data　and　auxiliary　data　to　form　training　data.　The　training　data　is　trained　based　on　the　iterative　adjustment　of　the　weight　through　the　TrAdaBoost　transfer　learning　algorithm.　Finally,　the　results　of　diagnosis　and　classification　are　compared　with　traditional　machine　learning.　When　the　number　of　training　data　is　5　groups,　the　accuracy　of　transfer　learning　is　30.5%　higher　than　that　of　traditional　machine　learning.　The　results　show　that　transfer　learning　has　great　advantages　in　the　case　of　a　small　number　of　samples.