During　natural　disasters,　public　opinion　guidance　contributes　to　maintaining　social　stability.　Social　media　is　an　important　channel　for　the　dissemination　of　public　opinion.　Understanding　users＇　network　emotions　and　topics　of　concern　through　microblog　comments　can　help　relevant　public　opinion　monitoring　departments　master　the　hot　spots　of　public　concern,　so　as　to　select　appropriate　intervention　nodes　to　deal　with　network　public　opinion　and　dredge　public　emotions,　which　is　of　practical　significance　for　emergency　management.　Most　of　the　existing　researches　use　supervised　machine　learning　methods　for　emotion　classification,　which　requires　manual　labeling　of　corpus,　and　the　workload　is　large.　While　the　unsupervised　methods　are　mainly　based　on　the　existing　emotional　dictionary,　which　can　reflect　the　unstructured　characteristics　of　the　text　and　is　easy　to　understand　and　explain.　According　to　the　characteristics　of　microblog　comments,　this　paper　constructs　an　emotional　dictionary　in　the　field　of　typhoon　disaster　by　comprehensively　considering　multiple　emotional　sources　such　as　emotional　words　and　emoticons.　Based　on　this,　this　paper　proposes　a　method　to　calculate　emotional　tendency　based　on　semantic　rules　of　emotional　words　and　a　topic　clustering　method　based　on　word　vector.　Firstly,　this　study　collected　a　total　of　more　than　400　000　comments　on　Sina　Weibo　during　five　times　typhoon　disasters　in　recent　years　and　constructed　the　emotional　dictionary　in　the　field　of　typhoon　disaster　based　DUTIR.　We　built　the　expression　symbol　dictionary　combined　with　the　Pointwise　Mutual　Information　method.　We　determined　the　emotional　tendencies　according　to　the　semantic　rules,　and　we　used　3500　comments　to　demonstrate　the　effectiveness　of　the　proposed　method.　Secondly,　based　on　the　clustering　method　of　word　vector,　TF-IDF,　and　K-means,　we　explored　the　hot　topics　during　these　disasters.　Finally,　taking　typhoon　Hagupit,　the　fourth　typhoon　in　2020,　as　an　example,　this　paper　conducted　an　analysis　on　more　than　50　000　Weibo　comments　during　the　typhoon　disaster,　and　identified　6　categories　of　typhoon-related　topics.　Through　the　spatial-temporal　analysis,　it　was　found　that　the　number　of　comments　on　Weibo　changed　as　time　went　on,　and　the　areas　with　a　large　number　of　comments　were　also　concentrated　in　coastal　areas　and　areas　with　high　economic　level.　On　the　day　of　typhoon　Hagupit　landing,　the　fear　in　Zhejiang　province　reached　the　highest　level.　The　results　show　that　the　typhoon　disaster　network　emotion　analysis　method　based　on　semantic　rules　and　word　vector　can　provide　assistance　for　government　departments　to　master　and　guide　network　public　opinion　when　similar　disaster　events　occur.　©　2022,　Science　Press.　All　right　reserved.