In　practical　rotating　machinery　fault　diagnosis　(FD)　tasks,　the　scarcity　of　labeled　fault　data　often　impedes　the　model＇s　ability　to　learn　effective　classification　boundaries,　resulting　in　poor　performance　with　limited　training　samples.　To　address　this　issue,　this　paper　introduces　a　rotation　machinery　few-shot　FD　algorithm　based　on　a　Relational　Attention　Network　(RAN).　The　network　comprises　four　modules:　feature　embedding,　feature　concatenation,　attention　computation,　and　relation　computation.　RAN　achieves　a　large-margin　feature　space　by　utilizing　extended　concatenation　and　attention　on　feature　pairs.　Specifically,　the　feature　embedding　module　extracts　signal　features　and　constructs　both　a　prototype-query　feature　pair　matrix　and　a　query-query　feature　pair　matrix.　Subsequently,　the　attention　computation　module　assigns　higher　weights　to　indistinguishable　heterogeneous　feature　pairs　within　the　two　matrices.　Finally,　the　attention-weighted　feature　pair　matrices　are　fed　into　the　relation　computation　module　to　generate　a　relation　score　matrix.　The　effectiveness　and　superiority　of　this　method　were　validated　on　a　bearing　fault　dataset.　Experimental　results　demonstrate　that　RAN　outperforms　various　comparison　methods　in　recognition　accuracy　under　limited　training　sample　conditions.　©　The　Institution　of　Engineering　&　Technology　2024.