Abstract: To quantitatively analyze the ability of social media network public opinion control, a network public opinion risk prediction method based on multi-attribute decision-making and comprehensive weight analysis is proposed. Firstly, web crawling methods are employed for data collection, and anti-interference matched filtering methods are used to clean the collected network public opinion data. Secondly, based on the preprocessed network media public opinion data, a multi-attribute comprehensive decision object model is constructed to obtain multiple quantifiable attribute sets, and word segmentation technology is used to decompose the text data into words. Based on the segmentation results, the association rules between the evolution of public opinion risks and people’s preferences are explored, and then the degree of association is calculated. Finally, the degree of association is fed as input into the BERT pre-trained vector model to obtain the directed feature values of network public opinion risks. By leveraging the evolutionary characteristics of network public opinion risks, predictions of their evolution are achieved. Simulation results demonstrate that the proposed  method exhibits strong optimization capabilities in predicting the evolution of network public opinion risks. The F1 comprehensive evaluation metric has improved compared to the standard methods, enhancing the accuracy of public opinion classification. Moreover, the prediction accuracy for the evolution of public opinion risks reached 97.6%.

Key words: social media, network public opinion, risk evolution, model design, multi-attribute decision