Abstract: 【Purpose/significance】In order to effectively judge the evolution trend of online public opinion, analyze the law of the
spread and formation of online public opinion,and study the status and characteristics of the evolution of online public opinion segmen-tation.based on AEMIPO method,this paper puts forward an analysis method of the evolution trend of network public opinion differenti-ation in the context of big data,so as to provide an optimized reference for the guidance strategy of network public opinion dissemina-tion【. Method/process】By dynamically tracking the statistical characteristics of the evolution process of online public opinion segmen-tation.such as self-similarity,periodicity and stability,select ARMA,ARIMA,SARIMA,FARIMA models to describe the above statisti-cal characteristics,and construct alternative models Library; Select the corresponding model from the candidate model library accord-ing to the selection rules to model the evolution trend of online public opinion segmentation,and predict the evolution trend of online public opinion segmentation in the context of big data【. Result/conclusion】The proposed method was validated and analyzed with the Weibo data of the "Shandong Gold Mine Explosion Rescue Case" as an example.The results showed that the prediction accuracy rate of the method was as high as 80%,indicating that the method can be used in the context of big data.The evolution trend of public opin-ion segmentation is accurately predicted and analyzed【. Innovation/limitation】Due to the analysis of an empirical case in this article.there are certain research limitations.Therefore,it is necessary to combine more cases for verification in future research and continue to optimize the method to improve its effectiveness and accuracy in all aspects degree.