In　order　to　effectively　identify　the　signs　of　instability　in　the　aerodynamic　system　of　an　axial　compressor,　a　wavelet　singular　spectral　entropy　algorithm　incorporated　within　the　wavelet　transform,　singular　value　decomposition　and　information　entropy　is　proposed　to　describe　the　distribution　complexity　of　the　spatial　modalities　in　the　flow　field.　This　kind　of　identification　design　can　accurately　distinguish　the　boundary　between　the　stable　and　unstable　states　of　the　internal　flow　field　from　the　view　of　a　dynamic　system.　On　the　basis　of　the　information　entropy　algorithm,　the　wavelet　singular　spectral　entropy　algorithm　is　designed　to　integrate　with　the　advantages　of　wavelet　transform　analysis　on　the　time-frequency　localization　and　singular　value　decomposition　for　signal　processing　and　data　mining　together.　So　that　the　quantitative　analysis　of　the　definition　of　rebuilding　a　system　image　can　be　achieved　by　the　solution　of　wavelet　singular　spectral　entropy.　This　method　can　automatically　extract　the　transient　information　of　the　space　mode　in　the　time-frequency　domain.　It　effectively　avoids　the　shortcoming　that　the　feature　extraction　on　spatial　information　cannot　be　accomplished　from　multiple　angles　with　the　single　information　entropy　algorithm.　In　the　data　processing　of　instability　signals　under　different　speeds,　the　wavelet　singular　spectral　entropy　algorithm　shows　a　greater　advantage　in　the　early　warning　for　compressor　stall.　The　result　shows　that　the　value　of　the　wavelet　singular　spectral　shows　an　obvious　mutation　when　the　aerodynamic　system　approaches　the　instability　boundary.　According　to　the　threshold　set,　the　identification　hybrid　algorithm　can　detect　the　stall　precursor　about　23　similar　to　96　r　in　advance.　Compared　to　the　single　information　entropy　algorithm,　the　hybrid　wavelet　singular　spectral　entropy　algorithm　is　able　to　shift　to　an　earlier　precursor　identification　by　about　11　similar　to　82　r.　This　established　hybrid　identification　algorithm　accounts　for　the　nonlinearity　of　the　aerodynamic　system,　providing　a　new　perspective　for　the　nonlinear　system　instability　identification.