Deep-learning-based　intelligent　diagnosis　is　a　popular　method　to　ensure　the　safe　operation　of　rolling　bearings.　However,　practical　diagnostic　tasks　are　often　subject　to　a　lack　of　labeled　data,　resulting　in　poor　performance　in　scenarios　with　insufficient　training　samples.　Moreover,　conventional　intelligent　diagnosis　methods　suffer　from　a　deficiency　in　interpretability.　In　this　article,　an　auto-embedding　transformer　(AET)　method　is　proposed　to　implement　the　interpretable　few-shot　fault　diagnosis　of　rolling　bearings.　First,　an　auto-embedding　module　is　developed　to　improve　the　embedding　quality　of　the　signal,　which　is　designed　based　on　a　novel　asymmetric　convolutional　encoder-decoder　architecture.　This　module　can　leverage　the　merits　of　unsupervised　learning　in　data　mining　and　allow　the　transformer　to　learn　more　diagnostic　knowledge　from　limited　data.　Second,　an　attention　scoring　method　is　proposed　that　utilizes　positionwise　attention　to　quantify　the　importance　of　each　signal　embedding　for　diagnosis,　thereby　interpreting　the　AET　method.　Experimental　results　confirm　that,　even　with　limited　training　samples,　the　AET　method　outperforms　various　comparison　methods　in　terms　of　recognition　accuracy　and　convergence　rate.　Furthermore,　the　attention　scores　assigned　to　each　embedding　facilitate　the　interpretability　of　the　AET　method.