Abstract: 【Purpose/significance】Explore the spread of the new media age short video network public opinion influence factors and dy⁃namic evolution, understand the short video the influence factors of the user and the dynamic evolution process, help the network pub⁃lic opinion supervision department in order to strengthen the management of short video network public opinion, for the related institu⁃tions or departments in the process of social governance to provide more timely and effective information services.【Method/process】Based on the theory of planned behavior, information behavior, by case study of new media agency official account, short video news corresponding to the user information data, set up the influencing factors of index weight and construction of new media short video net⁃
work public opinion transmission influence factor model; Comprehensive information, information technology, information environment and information subject multiple dimensions to measure short video transmission influence factors such as weight, analyze its spread effect; At the same time build a dynamic evolution model of short video communication behavior, and make the simulation of dynamic evolution process【. Result/conclusion】The research results show that information on new media short video network dissemination has significant impact on public opinion, short video network public opinion in new media to spread information technology impact is low, the dynamic evolution process of spread of the number of susceptible people and uninfected susceptible to converge, eventually even
with the new media are added to public opinion transmission no longer continue【. Innovation/limitation】From the perspective of multi⁃dimensional construct the influence factors of user behavior model, by using the simulation method to analyze the dynamic evolution of short video transmission, for the correct boot short video network public opinion transmission to provide scientific management basis.Future research will get a wider range of data sources for deeper network model research.