In　the　transient　stability　assessment,　machine　learning　based　model　has　the　problem　that　sample　imbalance　makes　the　model　have　a　certain　degree　of　evaluation　tendency.　From　the　loss　function　of　the　machine　learning　based　model,　it　is　found　that　the　fitting　degree　to　various　samples　after　training　can　be　mirrored　by　the　loss　function　value　of　the　samples.　Therefore,　a　cost-sensitive　strategy　based　on　the　imbalance　degree　of　the　loss　function　is　proposed.　Firstly,　the　machine　learning　based　model　is　trained　to　obtain　the　mean　value　of　loss　functions　of　various　samples.　Then,　the　sample　imbalance　degree　is　calculated　by　the　mean　ratio　of　the　loss　function　of　unstable　samples　to　that　of　stable　samples.　After　that,　the　loss　function　is　modified　by　the　sample　imbalance　degree　combined　with　the　cost-sensitive　strategy.　Finally,　to　remedy　the　assessment　inclination,　the　model　is　trained　once　again.　When　compared　to　conventional　approaches,　the　suggested　method　comprehensively　considers　the　impact　of　the　imbalance　in　quantity　and　spatial　distribution　of　samples.　This　model　achieves　higher　global　accuracy　and　greater　corrective　effect.　Simulation　findings　on　IEEE　39-bus　and　IEEE　145-bus　systems　are　used　to　confirm　the　usefulness　of　the　suggested　strategy.　©　2023　ACM.