To　solve　the　problem　of　cohesion　and　background-free　and　uneven　illumination,　which　makes　it　difficult　to　extract　direct　morphological　features　from　flotation　bubble　images,　a　multi-scale　equivalent　morphological　feature　extraction　and　recognition　method　for　flotation　bubbles　was　proposed　in　a　nonsubsampled　contourlet　transform　(NSCT)　domain.　Firstly,　the　flotation　bubble　image　was　decomposed　via　NSCT　to　obtain　a　low　frequency　subband　and　multi-scale　and　multi-directional　high　frequency　subbands.　The　fuzzy　set　method　was　used　for　the　binarization　of　the　low　frequency　subband　image　to　obtain　the　bubble　bright　spot　image,　and　the　number　of　bright　spots,　average　area,　standard　deviation,　and　ellipticity　were　extracted　as　the　equivalent　morphological　size　features.　Thereafter,　the　directional　modulus　maxima　and　differential　box-counting　method　were　used　to　calculate　the　fractal　dimensions　of　the　high　frequency　subband　directions.　Finally,　by　using　the　multi-scale　and　multi-directional　equivalent　morphological　size　features　as　the　input,　the　state　recognition　and　classification　of　three　types　of　flotation　bubble　images　were　carried　out　via　a　quantum　gate　node　neural　network.　The　experimental　results　show　that　the　extracted　equivalent　morphological　size　features　are　highly　correlated　with　the　classification　and　it　can　be　effectively　used　to　recognize　the　state　of　three　types　of　flotation　bubble　images.　The　average　recognition　accuracy　is　95.1%,　which　is　higher　than　that　of　several　common　algorithms,　and　it　is　suitable　for　dynamic　flotation　conditions.　©　2020,　Science　Press.　All　right　reserved.