为量化分析社交媒体网络舆情控制能力，提出基于多属性决策和综合权重分析的网络舆情风险预测方法。首先，选择网络爬虫方法进行数据采集，对采集到的网络舆情数据采用抗干扰的匹配滤波方法对其进行数据清洗。其次，针对预处理后的网络媒体舆情数据，构建多属性综合决策对象模型，以获取多个可量化的属性集合，并采用分词技术将文本数据分解为词语。基于分词结果，挖掘出舆情风险演化与人们喜好之间的关联规则，进而计算得到关联度。最后，将关联度作为BERT预训练向量模型的输入，获取网络舆情风险指向特征值，利用网络舆情风险演化特征实现网络舆情风险演化预测。仿真结果表明，所提方法进行网络舆情风险演化预测的寻优能力较强，F1综合评价指标比标准方法有所提高，提高了舆情分类的准确性，并且舆情风险演化预测精度达到了97.6%。

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%.