The　unsupervised　domain-adaptive　vehicle　re-identification　approach　aims　to　transfer　knowledge　from　a　labeled　source　domain　to　an　unlabeled　target　domain;　however,　there　are　knowledge　differences　between　the　target　domain　and　the　source　domain.　To　mitigate　domain　discrepancies,　existing　unsupervised　domain-adaptive　re-identification　methods　typically　require　access　to　source　domain　data　to　assist　in　retraining　the　target　domain　model.　However,　for　security　reasons,　such　as　data　privacy,　data　exchange　between　different　domains　is　often　infeasible　in　many　scenarios.　To　this　end,　this　paper　proposes　an　unsupervised　domain-adaptive　vehicle　re-identification　method　based　on　source-free　knowledge　transfer.　First,　by　constructing　a　source-free　domain　knowledge　migration　module,　the　target　domain　is　consistent　with　the　source　domain　model　output　to　train　a　generator　to　generate　the　＂source-like　samples＂.　Then,　it　can　effectively　reduce　the　model　knowledge　difference　and　improve　the　model＇s　generalization　performance.　In　the　experiment,　two　mainstream　public　datasets　in　this　field,　VeRi776　and　VehicleID,　are　tested　experimentally,　and　the　obtained　rank-k　(the　cumulative　matching　features)　and　mAP　(the　mean　Average　Precision)　indicators　are　both　improved,　which　are　suitable　for　object　re-identification　tasks　when　data　between　domains　cannot　be　interoperated.