This paper introduces the Integrated Media Intelligent Management Model, which leverages large scale model computing power for autonomous video content management. The model aims to address the challenges faced by traditional manual audit systems in managing massive audio-visual content, such as increased information volume and diverse content forms. The research focuses on efficient and accurate correlation calculation and unified management of audio-visual content, enabling functions like video content filtering, digital copyright management, and public opinion analysis. The model employs technologies such as Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Computer Vision (CV), and Content Delivery Network (CDN) to form a comprehensive audio visual content management platform. It also proposes a foundational information extraction and comparison model for ultra-large-scale broadcasting and audio-visual content, facilitating cross-platform content association analysis and providing decision-making references. Overall, this study contributes to the development of intelligent media services and the efficient management of audio visual content.