Abstract:  The prediction of the evolution trend of network public opinion has very important practical significance for the relevant government departments to supervise the development of public opinion and maintain the stability of public opinion in today's network environment. Aiming at the particularity of network public opinion data and considering the accuracy of model prediction results， this paper uses variational mode decomposition （VMD） and improved golden jackal optimization support vector regression （IGJO-SVR） to construct a network public opinion evolution trend prediction model， and takes ‘Beixi’ event-related public opinion data as a case for empirical research. The comparison results show that the accuracy of the prediction model constructed in this paper is significantly better than the other models. The network public opinion heat prediction model based on variational mode decomposition VMD and IGJO-SVR has excellent prediction accuracy， and can provide effective public opinion situation analysis and decision-making help for relevant government departments in practical work.

Key words: network public opinion, variational mode decomposition, golden jackal optimization algorithm, support vector regression, early warning mechanism