WiFi　fingerprint-based　localization　has　attracted　significant　research　interest　recently　because　WiFi　devices　were　widely　deployed　and　practicable.　The　accuracy　of　indoor　positioning　based　on　single　fingerprint　pattern　is　limited　since　it　is　susceptible　to　external　influences.　This　paper　proposes　a　multi-fingerprint　and　multi-classifier　fusion(MFMCF)　localization　method,　which　improves　the　localization　accuracy　by　constructing　multi-pattern　fingerprints　and　integrating　multiple　classifier.　MFMCF　constructs　signal　strength　difference(SSD),　hyperbolic　location　fingerprint(HLF)　and　received　signal　strength(RSS)　as　a　composite　fingerprint　set(CFS)　using　linear　discriminant　analysis(LDA).　A　special　decision-structure　of　multiple　classification　was　designed　by　calculating　the　entropy　of　the　classifiers　including　K-Nearest　Neighbor(KNN),　Support　Vector　Ma-chine(SVM)　and　Random　Forest(RF),　to　obtain　a　more　accurate　estimate　result.　Experiments　show　that　MFMCF　has　higher　localization　accuracy　and　robustness　to　single　fingerprinting　pattern.　©　2019　IEEE.