Rolling　bearings　are　one　of　the　most　important　components　of　rotating　machinery,　to　ensure　stability,　reliability,　and　safety　of　its　operation,　intelligent　fault　diagnosis　plays　a　vital　role.　In　the　industry,　the　condition　of　class　imbalance　and　few　fault　samples　exists　widely　in　the　fault　diagnosis　field.　In　order　to　diagnose　the　state　of　bearings　accurately　under　the　kind　of　conditions,　this　paper　proposes　a　one-dimensional　Branch　Convolutional　Neural　Network　with　Wide　first-layer　kernel　(BWCNN)　to　reduce　the　impact　of　data　imbalance　and　few　fault　samples.　First,　the　normalization　was　applied　to　the　original　time-domain　data　to　attenuate　the　effect　of　distribution　differences　of　different　features　on　the　model　and　improve　the　convergence　speed　of　the　model.　Then,　using　a　two-branch　network　constructed　by　a　one-dimensional　convolutional　network　with　a　first-layer　wide　convolutional　kernel,　the　model　parameters　was　iteratively　updated　during　the　training　phase　of　the　network　with　resampling　strategy　and　a　cumulative　learning　strategy,　making　it　possible　for　the　model　to　learn　generic　patterns　for　all　classes　of　samples　and　fault　patterns　for　a　few　classes.　Finally,　the　trained　model　was　used　for　fault　diagnosis.　The　proposed　method＇s　performance　was　evaluated　using　two　widely　used　experimental　datasets.　On　the　selected　datasets,　the　average　macro　F1　score　reaches　99.5%　when　the　healthy　samples　on　the　training　set　divided　by　the　sum　of　all　fault　samples　on　the　training　set　equals　20,　and　the　number　of　samples　for　each　class　of　faults　on　the　training　set　is　10.　©　2023　IEEE.