Recently,　deep　learning　technology　was　successfully　applied　to　mechanical　fault　diagnosis.　The　convolutional　neural　network　(CNN),　as　a　prevalent　deep　learning　model,　occupies　a　place　in　intelligent　fault　diagnosis,　which　reduces　the　need　for　human　feature　extraction　and　prior　knowledge,　thereby　achieving　an　end-to-end　intelligent　fault　diagnosis　model.　However,　the　data　for　mechanical　fault　diagnosis　in　practical　application　are　limited,　the　CNN　model　is　too　deep　and　too　complex,　making　it　prone　to　overfitting,　and　a　model　with　too　simple　a　structure　and　shallow　layers　cannot　fully　learn　the　effective　features　of　the　data.　Convolutional　filters　with　fixed　window　sizes　are　widely　used　in　existing　CNN　models,　which　cannot　flexibly　select　variable　pivotal　features.　The　model　may　be　interfered　with　by　redundant　information　in　feature　maps　during　training.　Therefore,　in　this　paper,　a　novel　shallow　multi-scale　convolutional　neural　network　with　attention　is　proposed　for　bearing　fault　diagnosis.　The　shallow　multi-scale　convolutional　neural　network　structure　can　fully　learn　the　feature　information　of　input　data　without　overfitting.　For　the　first　time,　a　feature　attention　mechanism　is　developed　for　fault　diagnosis　to　adaptively　select　features　for　classification　more　effectively,　where　the　pivotal　feature　was　emphasized,　and　the　redundant　feature　was　weakened　through　an　attention　mechanism.　The　time　frequency　representations　as　the　input　of　the　model　were　obtained　from　the　vibration　time　domain　signals,　which　contain　the　complete　time　domain　and　frequency　domain　information　of　the　vibration　signals.　Compared　with　the　current　popular　diagnostic　methods,　the　results　show　that　the　proposed　diagnostic　method　has　fairly　high　accuracy,　and　its　performance　is　superior　to　the　existing　methods.　The　average　recognition　accuracy　was　99.86%,　and　the　weak　recognition　rate　of　I-07　and　I-14　labels　was　improved.