This　paper　mainly　studies　the　dynamic　identification　of　hot　topics　and　their　trend　prediction:　the　identification　methods　of　hot　topics　are　studied　from　the　two　dimensions　of　content　and　form;　the　trend　prediction　method　is　completed　from　the　two　dimensions　of　media　attention　and　emotional　tendency.　This　paper　develops　a　hot　topic　recognition　method　based　on　formal　feature　ranking.　This　paper　compares　the　advantages　and　disadvantages　of　traditional　methods,　and　proposes　a　high-frequency　co-occurrence　clustering　strategy　based　on　minimum　similarity,　which　effectively　solves　the　timeliness　requirements　of　real-time　dynamic　hot　spot　recognition.　Based　on　the　recognition　of　hot　topics,　this　article　will　jointly　complete　the　trend　prediction　of　hot　topics　from　the　changes　in　media　attention　and　emotional　orientation.　We　have　encapsulated　the　hot　topic　recognition　method　based　on　high-frequency　co-occurrence　into　a　module　and　applied　it　in　the　national　language　and　writing　public　opinion　monitoring　system.　©　2021　IEEE.