Reshaping News Communication Algorithms with Software Defined Network Control and Management

In the field of news communication, the application of artificial intelligence technology, combined with software-defined network control and management of news communication algorithms, has driven a profound reshaping of news communication models. This paper constructs an advanced news production module based on natural language processing technology, which can accurately extract key information from massive data, significantly improving the accuracy and efficiency of news production. In addition, this paper integrates hybrid recommendation algorithms with LDA topic models and incorporates control and management mechanisms of software-defined networks, further enhancing the accuracy of personalized news recommendations. The experimental results show that compared to other models, the model proposed in this paper exhibits better performance in news generation and recommendation, and can maintain high stability and accuracy in different datasets, effectively improving users’ news click-through rates and browsing time. At the same time, the model greatly enriches the amount of news information, providing users with a more accurate personalized news service experience, and effectively enhancing the effectiveness and influence of news dissemination.