Abstract: With the rapid development of artificial intelligence, fusion media, mainly represented by Weibo, Wechat, B station and Tiktok, have become the main means of communication in the era of information explosion. The matrix spread of fusion media is extremely rapid, and the network space constructed by them has the characteristics of circle segmentation, social negativity and extreme behavior. In the important period of the formation of their outlook on life, world outlook and values, college students are extremely vulnerable to the impact of multi-culture and social trends of thought. They have always been the outbreak point of contradictions of social transformation and ethos trends. As the youngest and most active group of Internet users, the network behavior of “post-00s” college students and its characteristics are worthy of our study in depth. The research on fusion media network information push based on the perspective of network behavior tracking of “post-00s”college students is of great significance for universities and government departments to understand, supervise and govern the ideological dynamics of college students scientifically and precisely.
   Aiming at the shortcomings of existing research methods to deal with the preference relationship, in this paper, the intelligent hybrid push model of fusion media information is constructed based on the internal relationship between the information browsing habits and preferences of “post-00s” college students. An integrated model combining DEMATEL and TOPSIS methods with intuitive fuzzy numbers (IFN) is presented. In different time periods, the information set that college students are most concerned about is effectively sifted from a large number of network information. The internal correlation degree of information is extracted through analyzing the interaction between indicators and considering the level of criteria. The types of network information that college students pay most attention to in different time periods are identified. Then, a personalized preference model of college students’ financial media network information push is designed. Finally, the validity and applicability of the proposed method are verified by empirical data simulation. The research results show that the fusion media information push model based on the perspective of online behavior tracking of “post-00s” college students can carry out detailed differentiation calculation and classification screening for big data. It can provide the reference for network supervision departments to obtain, judge and filter information. It is of great theoretical and practical significance for universities and government departments to grasp the ideological dynamics of college students and fully and deeply participate in ideological and political education of college students under the environment of integrating media. It provides scientific and effective technical support for promoting the right to speak in cyberspace and establishing a community of shared future in cyberspace.
   In the actual process of network information push, the amount of network information is very large, and the classification of network information is also very important. Especially, in the age of artificial intelligence, it is more and more urgent for intelligent algorithms to drive the modernization of network governance. How to build an intelligent push algorithm for cloud computing is the next direction of research. In addition, the weight refinement of secondary indicators and the differential treatment of indicators are also worthy of further study. Therefore, in the future, we hope to put forward a decision-making method that is as fair as possible to the differentiation index system of college students’ Internet users’ preferences.

Key words: college students, network behavior tracking, fusion media, network information push

摘要： 作为网络用户中最为年轻活跃的群体,“00后”大学生的网络行为及其呈现出的特征规律值得我们深入研究。本文结合“00后”大学生网络用户的信息浏览习惯偏好的内在联系,构建融媒体信息智能混合推送模型。在不同的时间段,从海量的网络资讯中有效筛选出大学生最为关注的信息集合,通过分析指标之间的相互作用,考虑准则层次,提取出信息的内在关联程度,来提取出大学生在不同时间段最为关注的网络信息资讯种类,从而设计出大学生个性化偏好的融媒体网络信息推送模型。通过实证数据仿真,验证所提出方法的有效性和适用性。研究结果表明所提出的“00后”大学生网络行为追踪视域下的融媒体信息推送模型能够很好地针对大数据进行详细的区分计算和分类筛选,可以作为网络监管部门获取、判断和筛选信息的依据。

关键词: 大学生, 网络行为追踪, 融媒体, 网络信息推送