Aiming　at　the　iterative　characteristics　of　the　clustering　process　of　fuzzy　C-means　clustering　(CLUSTERING,　FCM)　algorithm,　an　iterative　MapReduce　model　is　used　for　FCM.　The　algorithm　is　optimized,　the　map　function　calculates　the　membership　degree　of　each　sample　to　the　cluster　center,　the　reduce　function　receives　the　new　cluster　center　of　the　middle　output　of　the　map　function,　and　the　transfer　module　transmits　the　newest　cluster　center　to　the　original　map　task　node　for　the　new　round　of　mapreduce　job;　iterative　The　MapReduce　model　adds　a　transfer　module　to　the　MapReduce　basic　model,　which　effectively　solves　the　deficiencies　of　the　basic　model　in　dealing　with　the　iterative　problem.　In　the　Hadoop　platform,　we　use　FCM　based　on　the　iterative　MapReduce　and　MapReduce　basic　models,　respectively.　The　algorithm　is　used　to　diagnose　the　transformer;　the　experimental　results　show　that　the　diagnostic　speed　of　FCM　algorithm　based　on　iterative　mapreduce　is　more　than　12　times　of　the　MapReduce　basic　model　algorithm,　the　misjudgment　rate　is　lower　12　to　15　percent,　and　the　diagnostic　efficiency　of　FCM　algorithm　is　improved　effectively.