User　influence　analysis　in　social　media　has　attracted　tremendous　interest　from　both　the　sociology　and　social　data　mining.　It　is　becoming　a　hot　topic　recently.　However,　most　approaches　ignore　the　temporal　characteristic　that　hidden　behind　the　comments　and　articles　of　users.　In　this　paper,　we　introduce　a　Tensor　Factorization　based　on　User　Cluster　(TFUC)　model　to　predict　the　ranking　of　users＇　influence　in　micro　blogs.　Initially,　TFUC　obtain　an　influential　users　cluster　by　neural　network　clustering　algorithm.　Then,　TFUC　choose　influential　users　to　construct　tensor　model.　A　time　matrix　restrain　TFUC　expect　CP　decomposition　and　ranked　users　by　their　influence　score　that　obtained　from　predicted　tensor　at　last.　Our　experimental　results　show　that　the　MAP　of　TFUC　is　higher　than　existing　influence　models　with　3.4%　at　least.