Big data is profoundly changing the news industry’s production logic and dissemination mode. Nowadays, the media increasingly rely on data mining, machine learning, and user behavior analysis to accurately predict audience concerns and optimize content presentation. The rise of data-driven journalism has enabled media outlets to adjust their topic selection strategies based on real-time data, increase the appeal of their stories, and improve production efficiency and scale through automation. However, this model is not without its drawbacks. Traffic orientation may lead media to cater to algorithmic recommendations overly, undermine in-depth reporting, and even contribute to the proliferation of emotional and vulgar content. In contrast, the role of big data in news dissemination is more intuitive. Precision communication allows media to customize news distribution based on audience reading habits and interests, improve user engagement and information reach efficiency, and enhance the social impact of news. This paper will discuss how big data reshapes news selection and content production and analyze its challenges and pitfalls.