AI-Enabled Content Optimization Mechanisms and Ethical Norms for the Translation and Dissemination of Chinese Culture in Japan

The translation and dissemination of Chinese culture in Japan face persistent challenges involving cultural imagery attenuation, weak contextual adaptation, and insufficient ethical review of AI-generated output. To address these problems, this study constructs a Chinese-Japanese cultural parallel corpus and knowledge graph, vectorizes culturally loaded expressions, and introduces a cognitive-anchor remapping strategy to reduce metaphorical shift in cross-cultural translation. On the basis of Japanese natural-language corpora, an audience cognitive preference model is trained to optimize register softening, honorific hierarchy, and the narrative structure of translated content. A multimodal style cooptimization procedure is further combined with a three-dimensional ethical review module, in which rule-based interception and manual verification form a closed control loop. Experimental results show improved cultural imagery targeting, audience reach, and control of sensitive expressions. The study frames cultural translation as an AI-assisted semantic transmission and communication optimization task, offering technical support for multilingual digital dissemination systems and multimodal content propagation.