In　actual　engineering　scenarios,　bearing　fault　signals　are　inevitably　overwhelmed　by　strong　background　noise　from　various　sources.　However,　most　deep-learning-based　diagnostic　models　tend　to　broaden　the　feature　extraction　scale　to　extract　rich　fault　features　for　bearing-fault　identification　under　noise　interference,　with　little　attention　paid　to　multi-timescale　discriminative　feature　mining　with　adaptive　noise　rejection,　which　affects　the　diagnostic　performance.　Thus,　a　multi-timescale　attention　residual　shrinkage　network　with　adaptive　global-local　denoising　(AMARSN)　was　proposed　for　rolling-bearing　fault　diagnosis　by　learning　discriminative　multi-timescale　fault　features　from　signals　and　fully　eliminating　noise　components　in　the　multi-timescale　fault　features.　First,　a　multi-timescale　attention　learning　module　(MALMod)　was　developed　to　capture　multi-timescale　fault　features　and　enhance　their　discriminability　under　noise　interference.　Subsequently,　an　adaptive　global-local　denoising　module　(AGDMod)　was　constructed　to　fully　eliminate　noise　in　multiscale　fault　features　by　constructing　specific　global-local　denoising　thresholds　and　designing　an　adaptive　smooth　soft　thresholding　function.　Finally,　end-toend　bearing　fault　diagnosis　tasks　were　realized　using　a　softmax　classifier　located　at　the　end　of　the　AMARSN.　The　AMARSN　was　validated　using　two　bearing　datasets.　The　extensive　results　demonstrated　that　the　AMARSN　can　mine　more　effective　fault　features　from　signals　and　achieve　average　diagnostic　accuracies　of　85.24%　and　80.09%　under　different　noise　with　different　levels.