With　the　rapid　development　of　information　technology,　the　problem　of　name　ambiguity　has　become　one　of　the　main　problems　in　the　fields　of　information　retrieval,　data　mining　and　scientific　measurement,　which　inevitably　affects　the　accuracy　of　information　calculations,　reduces　the　credibility　of　the　literature　retrieval　system,　and　affect　the　quality　of　information.　To　deal　with　this,　name　disambiguation　technology　has　been　proposed,　which　maps　virtual　relational　networks　to　real　social　networks.　However,　most　existing　related　work　did　not　consider　the　problem　of　name　coreference　and　the　inability　to　correctly　match　due　to　the　different　writing　formats　between　two　same　strings.　This　paper　mainly　proposes　an　algorithm　for　Author　Name　Disambiguation　based　on　Molecular　Cross　Clustering　(ANDMC)　considering　name　coreference.　Meanwhile,　we　explored　the　string　matching　algorithm　called　Improved　Levenshtein　Distance　(ILD),　which　solves　the　problem　of　matching　between　two　same　strings　with　different　writing　format.　The　experimental　results　show　that　our　algorithm　outperforms　the　baseline　method.　(F1-score　9.48%　21.45%　higher　than　SC　and　HAC).