It＇s　a　challenging　work　to　diagnose　faults　from　the　measured　vibration　signals　automatically　and　efficiently　under　small　samples.　A　new　intelligent　fault　diagnosis　method　of　rolling　bearing　with　small　samples　is　proposed　based　on　structural　similarity　generative　adversarial　network　(SSGAN)　and　improved　MobileNetv3　convolutional　neural　network　(IMCNN).　Firstly,　the　wavelet　transform　(WT)　is　performed　on　the　signal　to　obtain　a　wavelet　2D　image　with　time-frequency　characteristics.　Then,　SSGAN　is　constructed　to　obtain　high-quality　generated　samples　for　expanding　the　small　training　sets.　Finally,　the　improved　MobileNetv3　convolutional　neural　network　(IMCNN)　is　proposed　to　extract　feature　information　of　the　extended　samples　by　using　the　self-focus　mechanism　instead　of　the　original　lightweight　focus　mechanism,　and　the　classification　results　are　acquired　for　fault　recognition.　The　experimental　results　show　that　the　proposed　SSGAN-IMCNN　method　can　effectively　extend　the　small　samples　and　automatically　detect　the　rolling　bearing　faults　with　high　classification　accuracy.