Deep　learning　algorithms　have　excellent　performance　in　power　system　transient　stability　assessment,　but　the　incomprehensibility　of　the　assessment　results　and　the　uncontrollability　of　the　decision-making　process　hinder　their　practical　adoption　by　industry.　A　transient　stability　assessment　model　based　on　the　Transformer　encoder　is　proposed.　The　rules　that　the　model　focuses　on　and　learns　can　be　interpreted　and　analyzed　by　the　attention　weights　of　the　features.　Thus　a　model　updating　method　is　proposed　which　employs　physical　information　in　combination　with　interpretable　results　to　guide　model　optimization.　From　the　perspective　of　the　loss　function,　the　attention　weight　distribution　of　the　model　to　the　features　is　adjusted　in　a　fine-tuned　way　to　enhance　the　mining　for　the　instability　patterns.　In　the　process　of　fine-tuning,　an　attention-guiding　function　is　introduced　to　increase　the　attention　weights　to　the　key　generators　of　specific　instability　patterns,　so　as　to　reduce　the　misclassification　of　specific　instability　patterns.　In　this　way　the　overall　prediction　accuracy　can　be　improved.　The　performance　of　the　proposed　method　is　verified　on　the　IEEE39-bus　system　and　the　East　China　power　grid　system.　©　2023　Power　System　Protection　and　Control　Press.　All　rights　reserved.