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1、AbstractWiththerapiddevelopmentofIntemettechnology,micro—bloghasbecomeanimportantpartofpeople’Slives．Publicopinioncausedbythemicro—blogattractsmoreandmorepeople’Sattention．Sincethemicro—bloginformation’Spropagationvelocityisquickly,spreadwidelyandarbitrary
2、natureofmicro—blog，SOtheinformationonthemicro-bloghastrueandfalse．Positiveandnegativemicro—blogpublicopinionwillhavedifferenteffectsonpeople’Slives，someofthenegativemicro—blogpublicopinionwillevenconstituteacrisis，thenaseriousimpactonpublicsafety．Therefore
3、，thestudyonpredictedmicro—blogpublicopinionhasapracticalsignificance．Predictedmicro—blogpublicopinion，wemustgetdatawhichCanbeexpressedmicro—blogpublicopinionfirstly．Thepaperusesthediscretetimeseriestodescribemicro—blogpublicopinion’Strends．Inthispaper,weus
4、eSinamicro-blogplatformforbackground，accordingthehotmicro—blogtopictextextraction，analysis，forecastmicro—blogpublicopinion．Gettimeseriesstep：one誦tllSinamicro—blogAPIinterface，accesstomicro—blogtogetmicro—blogtextinsometime；Second，accordingthecharacteristic
5、softhecorrespondingpretreatmentmicro—blogtext，usethemethodsofstatisticalmicro-blogtopicandfoundthatmicro—bloghottopic；Third，statisticalthenumberofrepliesandforwardingnumberofmicro—bloghottopicforsometime，andusethenumbertoconsistofpublicopinionpredictionmod
6、el’Sexperimentaldata．BPneuralnetworkCanbebetterfitthenonlinearvariationofmicro—blogpublicopinion’Stimeseries，whichcanbeusedtopredictthemicro—blogpublicopinion，buttherearesomelimitations：BPneuralnetwork’Slearningalgorithmhastheweaknessofforgettingthealready
7、learningsamples．Whenthereisnoiseinthesample，theremaycausepoorperformanceonB．Pneuralnetwork；BPneuralnetworkalsohasaslowspeedofconvergence，andeasytofallintolocalminima．Wedidtwotasks：First，changethenetwork’SstructuretoimproveBPneuralnetwork．BehindtheBPneuraln
8、etworkinputlayerneurons，weaddalayertostoretheinputlayer’Shistorydata．Whenthesampleshaveanoisedata，thelayercandelaynetworkparameterstoimprovetheperformanceofBPneuralnetwork．ThesecondistouseGSAtooptimizethenetw
