AIGC-Driven Reconstruction of Intelligent Media Content Production Workflow

Intelligent technologies centered on AIGC are fundamentally reshaping the paradigm of media content production. This paper reviews the limitations of traditional media production workflows and the latest advances in AIGC technologies, and innovatively proposes a five-stage reconstruction model for intelligent media content production featuring "human-machine collaboration, data-driven decision-making, and dynamic optimization". On this basis, a unified mathematical framework integrating diffusion models, LLMsand reinforcement learning is constructed, and a corresponding optimization algorithm is designed to achieve the Pareto optimality of content quality and production efficiency. Comparative experiments are designed to simulate the production tasks of three typical types of media content: news flashes, short video scripts and dynamic infographics. The results show that compared with the traditional workflow and the single-point AIGC-assisted workflow, the reconstructed workflow proposed in this paper exhibits significant advantages in key indicators such as content production efficiency, diversity index and user satisfaction.