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