Evaluation of Machine-Translated Subtitles for the Documentary China from a Cultural Translation Perspective

This study evaluates the English subtitle translation performance of two large language models, DeepSeek and Moonshot AI, for the documentary China Season 2 from the perspective of cultural translation. By collecting 875 subtitle data containing culture-loaded words, poems, and terms, and combining word frequency analysis, BLEU/ROUGE automated scoring, and in-depth case analysis, it is found that the two models show 63% consistency at the lexical semantic level, but differ significantly in phrasal structure and cultural strategy selection. DeepSeek excels in literal translation retention of calendrical terms such as "sui/si/zai " and rhythmic reconstruction of Du Fu's poems, while Moonshot AI has an advantage in cultural interpretation of metaphors like "the smell of wine and meat from the vermilion gates " and contextual coherence of Li Bai's image. The study reveals problems such as semantic deviation and format norm defects in AI-based cultural subtitle translation, providing an empirical basis for constructing a "technology-culture " two-dimensional evaluation framework.