An　intelligent　bearing　fault　diagnosis　method　based　requires　a　large　quantity　of　labeled　data.　However,　in　an　actual　engineering　environment,　only　a　tiny　amount　of　unlabeled　data　can　be　collected.　To　solve　this　problem,　we　construct　a　spatio-temporal　neural　network　(STN)　model　by　multi-layer　fusion　of　convolutional　neural　network　(CNN)　and　long-term　memory　network　features.　Then,　a　model　based　on　feature　migration　is　constructed　and　a　STN　is　applied　as　the　feature　extractor　of　the　network.　Finally,　the　Case　Western　Reserve　University　bearing　dataset　is　employed　to　verify　the　performance　of　our　proposed　model,　and　the　influence　of　different　neural　network　feature　extractors　(CNN,　recurrent　neural　network,　long-　and　short-term　memory　network,　STN)　and　several　feature　transfer　measures　[correlation　alignment,　multiple　kernel　maximum　mean　discrepancy,　joint　maximum　mean　discrepancy,　discriminative　joint　probability　maximum　mean　discrepancy　(DJP-MMD)　on　the　accuracy　of　the　model　were　compared.　The　results　show　that　the　diagnostic　accuracy　of　the　proposed　method　is　over　98%,　and　the　diagnostic　accuracy　can　be　maintained　at　around　99%　in　most　cases　when　the　signal　to　noise　ratio　(SNR)　is　10　dB.　When　the　SNR　is　lower　than　2　dB,　the　accuracy　of　the　STN-DJPMMD　model　is　still　over　88%.