Rolling　bearing　vibration　signals　exhibit　typically　complex　modulation　characteristics,　and　usually　present　nonstationary　features.　The　defect　of　a　rolling　bearing　is　mainly　manifested　by　impulses　carried　by　resonance　vibration,　and　the　resonance　frequency　is　independent　of　the　operation　conditions.　Recent　studies　have　correlated　the　characteristics　of　impulses　with　the　fault　severity　of　rolling　bearings.　However,　the　impulses　are　extracted　manually,　and　the　fault　severity　is　evaluated　by　manually　analyzing　the　target　impulses　or　the　matched　atoms.　This　paper　takes　advantage　of　impulses,　and　proposes　a　novel　intelligent　rolling　bearing　fault　severity　recognition　method.　The　method　includes　two　modules,　i.e.,　impulse　mining　and　fault　recognition.　Recently,　matrix　profile　(MP)　has　emerged　as　a　promising　method　of　mining　the　motifs　in　a　time-domain　signal.　In　the　first　module,　MP　is　firstly　introduced　to　the　field　of　fault　diagnosis,　and　it　is　conducted　to　mine　the　impulses　from　vibration　signals.　In　the　second　module,　convolutional　neural　network　(CNN)　combined　with　softmax　regression　is　applied　to　automatically　extract　the　discriminatory　features　from　the　mined　impulses　and　accomplish　the　fault　severity　recognition.　The　proposed　method　is　evaluated　by　the　lab　experimental　bearing　signals,　and　further　validated　by　the　signals　collected　from　a　wind　turbine　simulator　with　faulty　high-speed　shaft　roller　bearing.　The　results　demonstrate　that　the　proposed　method　can　better　recognize　the　fault　severities　of　rolling　bearings　than　the　comparison　methods.