Affiliations: Institute of Economic and Social Research, Jinan University, Guangzhou, China
Correspondence:
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Corresponding author. Xu Mengyao, Institute of Economic and Social Research, Jinan University, Guangzhou, 510632, China. E-mail: [email protected].
Abstract: Online news media websites and mobile news applications can report hot events in real time. With the change of news duration, we urgently need a tool that can automatically extract hot events from massive data and show how events change dynamically with time. In this paper, the authors analyze the news transmission mode based on fuzzy data classification and neural network simulation. Due to the limitation of BP neural network algorithm, there are some problems in the prediction of information transmission. Therefore, we use fuzzy algorithm to optimize BP neural network, which is easy to fall into local minimum and slow convergence speed, so that BP neural network has higher prediction accuracy. The simulation results show that after the introduction of the neural network model, the features of neural network with richer semantic information can be used, and the new event line can be processed at the same time. The training speed of news content processing is much faster than that of probability graph model. It can be seen that under the influence of new media, news communication shows new characteristics, which further affects people’s news reading habits.
Keywords: Data prediction, neural network, news information, intelligent search
