An　updating　method　for　data-driven　based　transient　stability　assessment　model　of　power　systems　is　proposed　in　this　paper.　To　cope　with　potential　faults　in　the　real-time　operation　of　power　systems,　the　parameters　of　models　can　be　modified　online　through　the　proposed　method.　Firstly,　according　to　the　long-short　term　memory　(LSTM)　network,　transient　stability　assessment　models　are　initially　trained　offline　by　expected　faults.　Then,　the　feature　distribution　differences　between　expected　faults　and　potential　faults　are　evaluated　by　the　time-sequence　maximum　mean　discrepancy　(TMMD).　Compared　with　the　traditional　maximum　mean　discrepancy　(MMD),　the　proposed　TMMD　approach　takes　the　temporal　properties　of　transient　stability　into　account,　which　can　reflect　the　relationship　between　fault　samples　and　time　series　better.　Finally,　the　parameters　of　models　are　adjusted　by　transfer　learning　to　reduce　the　feature　distribution　differences,　by　which　the　updated　models　can　be　more　suitable　for　the　different　but　related　potential　faults.　Therefore,　through　the　proposed　model　updating　method,　the　extensibility　of　evaluation　models　is　greatly　enhanced,　which　is　more　in　line　with　the　practical　conditions　of　power　systems.　In　this　paper,　the　effectiveness　of　the　proposed　method　has　been　demonstrated　by　the　evaluation　results　in　the　IEEE　39-bus　system　and　a　realistic　power　system.