With　the　rapid　development　of　machine　learning　technology,　a　new　tool　is　provided　for　real-time　stability　evaluation　in　power　systems.　The　training　of　a　machine　learning-based　model　is　inseparable　from　a　large　number　of　training　samples.　However,　compared　with　stable　samples,　unstable　samples　in　power　systems　are　infrequent.　The　results　of　the　model　evaluation　are　biased　due　to　the　imbalance　of　training　samples.　Faced　with　such　a　problem,　a　framework　based　on　deep　imbalanced　learning　is　proposed.　Firstly,　for　each　sample,　the　samples　nearby　in　the　opposite　class　are　applied　to　calculate　its　space　information.　Based　on　the　space　information　of　all　samples,　the　spatial　distribution　characteristics　of　the　training　samples　are　obtained.　And　then,　in　order　to　obtain　balanced　training　samples,　unstable　samples　are　generated　according　to　their　spatial　distribution　characteristics.　Finally,　stacked　sparse　denoising　auto-encoder　(SSDAE)　based　model,　which　has　the　ability　of　anti-noise,　is　established　as　the　classifier.　Simulation　results　in　IEEE　39-bus　system　show　the　high　performance　of　the　proposed　imbalanced　correction　scheme　and　evaluation　scheme.　©　The　Institution　of　Engineering　and　Technology　2020