The　cross-domain　diagnosis　method　based　on　transfer　learning　(TL)　provides　a　solution　for　fault　diagnosis　of　the　aircraft　fuel　pump.　However,　due　to　the　fuel　pump　working　in　harsh　conditions,　unknown　faults　or　similar　faults　that　are　difficult　to　distinguish　are　likely　to　occur.　The　diagnosis　results　determine　whether　effective　maintenance　measures　can　be　taken.　To　overcome　these　weaknesses,　in　this　article,　a　class　adaptive　TL　network　(CATLN)　is　proposed　for　detecting　similar　and　unknown　faults　of　aircraft　fuel　pumps.　First,　according　to　the　characteristics　of　the　collected　vibration　signals,　a　multibranch　feature　extractor　is　constructed　to　extract　multiscale　features　in　time　domain　and　frequency　domain.　Second,　a　class　adaptive　method　is　designed　to　reduce　the　interclass　distance　of　the　same　class　by　dividing　the　core　area　of　the　sample,　and　increase　the　interclass　distance　of　different　classes　to　distinguish　similar　faults　and　unknown　faults.　The　fault　data　collected　in　the　ground　experiment　of　the　fuel　pump　are　used　for　transfer　tasks.　The　results　demonstrate　the　effectiveness　of　the　CATLN　in　the　cross-domain　fault　diagnosis　task　containing　similar　or　unknown　fault　classes.