Fuzzy　C-means　clustering　integration　algorithm　is　a　method　to　improve　clustering　quality　by　using　integration　ideas,　but　as　the　amount　of　data　increases,　its　time　complexity　increases.　A　parallel　FCM　clustering　integration　algorithm　based　on　MapReduce　is　proposed.　The　algorithm　uses　a　random　initial　clustering　centre　to　obtain　differentiated　cluster　members.　By　establishing　an　overlapping　matrix　between　clusters,　the　clustering　labels　are　unified　to　find　logical　equivalence　clusters.　The　cluster　members　share　the　classification　information　of　the　data　objects　by　voting　to　obtain　the　final　clustering　result.　The　experimental　results　show　that　the　parallel　FCM　clustering　integration　algorithm　has　good　performance,　and　has　high　speedup　and　good　scalability.