With　the　continuous　development　of　computer　technology,　deep　learning　has　been　widely　used　in　fault　diagnosis　and　achieved　remarkable　results.　However,　in　actual　production,　the　problem　of　insufficient　fault　samples　and　the　difference　in　data　domains　caused　by　different　working　conditions　seriously　limit　the　improvement　of　model　diagnosis　ability.　In　recent　years,　meta-learning　has　attracted　widespread　attention　from　scholars　as　one　of　the　main　methods　of　few-shot　learning.　It　can　quickly　adapt　to　new　tasks　by　training　on　a　small　number　of　samples.　A　fine-tuning　prototypical　network　is　proposed　on　meta-learning　methods　to　address　the　challenges　of　fault　diagnosis　under　few-shot　and　cross-domain.　Firstly,　the　shuffle　attention　is　used　to　enhance　the　feature　extraction　ability　of　the　network　and　suppress　irrelevant　features.　Then,　the　support　set　of　the　target　domain　is　split　into　two　parts:　pseudo　support　set　and　pseudo　query　set,　which　are　used　to　fine-tune　the　prototypical　network　and　improve　the　model　generalization.　Finally,　experiments　are　conducted　on　three　rotating　equipment　datasets　to　verify　the　method＇s　effectiveness.