With　the　phasor　measurement　units　(PMUs)　being　widely　utilized　in　power　systems,　a　large　amount　of　data　can　be　stored.　If　transient　stability　assessment　(TSA)　method　based　on　the　deep　learning　model　is　trained　by　this　dataset,　it　requires　high　computation　cost.　Furthermore,　the　fact　that　unstable　cases　rarely　occur　would　lead　to　an　imbalanced　dataset.　Thus,　power　system　transient　stability　status　prediction　has　the　bias　problem　caused　by　the　imbalance　of　sample　size　and　class　importance.　Faced　with　such　a　problem,　a　TSA　model　based　on　the　sample　selection　method　is　proposed　in　this　paper.　Sample　selection　aims　to　optimize　the　training　set　to　speed　up　the　training　process　while　improving　the　preference　of　the　TSA　model.　The　typical　samples　which　can　accurately　express　the　spatial　distribution　of　the　raw　dataset　are　selected　by　the　proposed　method.　Primarily,　based　on　the　location　of　training　samples　in　the　feature　space,　the　border　samples　are　selected　by　trained　support　vector　machine　(SVM),　and　the　edge　samples　are　selected　by　the　assistance　of　the　approximated　tangent　hyperplane　of　a　class　surface.　Then,　the　selected　samples　are　input　to　stacked　sparse　autoencoder　(SSAE)　as　the　final　classifier.　Simulation　results　in　the　IEEE　39-bus　system　and　the　realistic　regional　power　system　of　Eastern　China　show　the　high　performance　of　the　proposed　method.