The　failure　of　bearings　is　a　prevalent　cause　of　machinery　breakdowns.　The　rapid　development　of　intelligent　technology　has　significantly　promoted　the　use　of　deep　learning　techniques　for　identifying　problems　with　machinery　bearings.　To　achieve　accuracy,　deep　learning-based　diagnostic　techniques　require　a　substantial　and　uniformly　diversified　amount　of　training　data.　However,　obtaining　artificial　labels　for　bearing　fault　data　poses　a　major　obstacle　in　engineering　practice.　This　paper　proposes　an　intelligent　fault　diagnosis　method　for　bearings　based　on　an　improved　convolutional　neural　network　(CNN)　to　address　the　challenges　of　small　training　data　and　imbalanced　distribution.　To　enable　intelligent　diagnosis　of　bearings　with　a　small　sample　and　imbalanced　distribution,　a　clustering　loss　layer　is　introduced　into　the　CNN.　Furthermore,　we　optimize　the　parameters　of　the　CNN　by　utilizing　back-propagation　of　both　the　clustering　loss　function　and　the　cross-entropy　loss　function.　This　optimization　process　improves　the　accuracy　of　fault　diagnosis.　Finally,　the　proposed　method　is　applied　to　diagnose　bearing　faults　and　analyze　the　simulation　results.　The　simulation　results　demonstrate　the　effectiveness　of　the　method　in　handling　small　data　volumes　and　imbalanced　data　distributions,　as　well　as　its　strong　generalization　performance.