Research on visualization design of AIGC empowering ecological environment system management and green sustainable development

With the accelerating process of urbanization, air quality is gradually deteriorating, and the ecological environment as well as human health are facing severe challenges.This study addresses global ecological environment management and Green Sustainable Development needs by proposing a visualization solution based on Artificial Intelligence Generated Content (AIGC). It presents complex ecological management data in an intuitive and understandable form through visual design, aiming to improve ecological environment management efficiency, provide effective support for Green Sustainable Development decisions, and facilitate the resolution of ecological environmental issues. The research integrates core technologies such as Generative Adversarial Networks (GAN) to build an intelligent analysis framework for multi-source data fusion, enabling visual representation of dynamic ecological environment simulation and decision support. Taking Nanning's air quality environment as an empirical case, the study validates the application performance of optimized AI tools (DeepSeek, Kimi, Doubao) in predicting sulfur dioxide and PM10 concentrations. Results show: 1)Visual design integrated into air quality monitoring data management enhances data readability and comprehensibility; 2)DeepSeek achieves an average absolute percentage error of 0.1128% in ambient air quality prediction, improving accuracy by 31.01% and 43.98% compared to Kimi and Doubao, respectively. The AIGC technology has significantly improved the efficiency of environmental data processing. It has certain promotion value and application prospects both at home and abroad, providing an intelligent and visual innovative path for the systematic management of the ecological environment.