Practice, Problems and Optimization Paths of GAI Empowering Mainstream Media from News Types Perspective

Abstract

Generative Artificial Intelligence (GAI) technology has undergone continuous iteration and evolution, and is now deeply integrated into various practical aspects of the media industry, significantly transforming the traditional production models of mainstream media. This article focuses on five major categories of mainstream news-Political news, people’s livelihood, finance, culture and sports, and international communication-as its primary research subjects. By examining the current state of industry development, it outlines the specific applications and practical value of GAI in news creation, while addressing emerging challenges such as content homogenization, technical vulnerabilities, ambiguous delineation of responsibilities, and a shortage of specialized talent during implementation. The analysis further incorporates contemporary trends in intelligent communication and media convergence initiatives. This comprehensive framework establishes a holistic optimization system across four dimensions-content refinement, technological implementation, institutional development, and talent cultivation-providing practical and actionable guidance for the industry to advance standardized and high-quality GAI-driven news production.