Research on AI Adaptability Countermeasures of Live Transmission in Hetu Luoshu Under the Guidance of Science and Art Integration

In recent years, the integration of science and art has continuously promoted the dynamic inheritance of traditional culture into a new stage. This article focuses on the mechanism optimization and adaptation decisions of the active inheritance of Hetu Luoshu under the background of AI empowerment, attempting to break the one-way link of display replication and explore the multi-layer feedback between symbol semantics, user behavior, and AI self-learning. The team relies on multi-source data collection and simulation experiments to conduct in-depth modeling of key variables such as symbol features and interaction strength, analyze the adaptation peaks, valleys, mismatch faults of AI decision models under different mechanisms, and identify real bottlenecks and innovative points in the process of active inheritance. The study simultaneously introduces dynamic intervention paths such as user stratification and expert feedback and constructs an adaptive decision-making system that is both closed-loop and behavior driven.