In　recent　years,　with　the　rapid　development　of　Internet　technology,　the　spread　of　network　rumors　has　become　one　of　the　important　obstacles　to　maintain　the　stable　development　of　social　networks　and　ensure　the　public　security.　Most　of　the　existing　researches　focus　on　the　detection　of　rumors　in　general　fields,　ignoring　the　differences　among　different　fields.　According　to　the　characteristics　of　rumor　in　the　health　field,　this　paper　proposes　a　rumor　detection　method　based　on　topic　classification　and　multi-scale　fusion.　Different　methods　are　used　to　extract　features　from　different　sub　datasets　of　different　scales,　taking　into　account　the　overall,　inter　topic,　and　intra　subject　correlation　and　differences,　and　then　judge　after　feature　fusion.　The　experimental　results　show　that　this　method　is　better　than　the　general　detection　method　in　the　data　set　of　health　field,　and　has　some　improvement　compared　with　the　algorithm　in　the　same　field.　©　Published　under　licence　by　IOP　Publishing　Ltd.