News Influence Prediction Algorithm Based on Deep Data Mining

With the rapid development of social network, sensor technology and mobile Internet technology, it has become an important strategic resource for enterprises and even the country. With the active promotion of national policies, big data service platforms are emerging, which lays a good foundation for obtaining the required resources in the “data ocean” and mining the potential value behind the data. Most of the existing research on news user influence prediction is based on the global characteristics of microblog social networks, ignoring the local characteristics of heterogeneous nodes in social networks and the attributes and text information contained in the nodes themselves. In order to integrate richer social network features and improve the prediction performance of news user influence, this paper proposes a news user influence prediction model based on heterogeneous network representation learning. News reporting through data mining. After the complicated and trivial unstructured data, they found the logic that can not be reflected in the conventional news to help readers deeply interpret the news events. The news of data mining is often more powerful than traditional news reporting. It is of great help to improve the popularity by processing the news category, graphic setting and word application, selecting the decision attributes to construct the decision tree, predicting whether the news will become a hot spot, and providing an easy to implement optimization scheme.