Rolling　bearings　are　critical　and　easily　damaged　components　of　mechanical　equipment.　In　practical　engineering　applications,　the　collected　signals　typically　contain　a　large　amount　of　noise,　which　makes　fault　diagnosis　difficult.　Based　on　this,　this　paper　proposes　an　adaptive　time-frequency　demodulation　method　for　rolling　bearing　diagnosis.　The　proposed　method　first　obtains　the　complex　envelope　of　the　vibration　signal　in　the　time-frequency　domain　using　the　S　transform　(ST),　and　the　time-frequency　coefficient　of　ST　can　be　used　as　the　complex　envelope,　which　is　proved　in　detail　in　this　paper.　Subsequently,　the　complex　envelope　of　the　optimal　slice　frequency　is　obtained　by　frequency　slicing　to　significantly　weaken　the　interference　of　irrelevant　noise　and　highlight　the　fault　characteristics.　An　indicator　is　proposed　to　adaptively　select　an　optimal　slice　frequency　component　that　contains　the　most　fault　information.　Finally,　the　slice　envelope　spectrum　of　the　optimal　slice　frequency　is　obtained　using　Fourier　transform　for　fault　diagnosis.　The　feasibility　of　the　proposed　method　is　verified　using　the　simulated　signal.　The　application　results　of　the　bearing　inner　and　outer　ring　fault　experimental　signals　indicate　that　the　proposed　method　is　more　accurate　and　effective　for　bearing　fault　diagnosis.　Comparisons　with　other　commonly　used　methods　also　verified　the　superiority　of　the　proposed　method.