中文摘要：

[目的／意义]面向大数据研究网络舆情热度模型以及热度预测模型，能够准确把握大数据环境下网络舆情热度，并可以决定网络舆情应对和舆论引导措施的轻重缓急，具有重要的理论意义。[方法／过程]定性分析大数据环境下网络舆情热度影响因素，通过定义最大关联度向量，基于灰色关联度方法构建网络舆情热度模型，并在此基础上构建多维度logistic模型对各个媒体平台舆情信息开展预测，通过灰色关联度得出动态预测方法。[结论／结果]经过理论建模和实证分析得出构建的热度模型和热度动态预测模型是可行的，以上理论研究可为政府准确把握大数据环境下网络舆情热度，制定网络舆情引导策略提供参考依据。

英文摘要：

[ Purpose/Significance ] It is of important theoretical significance to conduct researches on the Internet public opinion hot-degree model and the hot-degree dynamic prediction model oriented to big data, which help grasp the network public opinion hot-degree accurately and determine the activities＇ priorities on guiding and controlling the development of the public opinions. [ Method/Process] This paper conducted a qualitative analysis of the factors of Interact public opinion hot-degree oriented to big data, and through defining the maximum relevance vector, built the Internet public opinion hot-degree model based on grey correlation method, then carded out further research on multidimensional logistic model of predicting Internet public opinion on various media platforms, which comes to a dynamic prediction model combined with gray correlation degree. [ Result/Conclusion] The feasibility of the two models was verified by an empiri- cal analysis and a theoretical modeling. The findings can provide significant reference for the government to grasp the Internet public opin- ion hot-degree oriented to big data and develop the appropriate Internet public opinion guiding strategies.