Abstract

To explore the capability boundaries and strategy preferences of generative artificial intelligence in the translation of science fiction literature, this paper takes Liu Cixin's "The Three-Body Problem II: The Dark Forest" as the corpus and adopts a case study approach to conduct a systematic analysis of the translation outputs of a specific artificial intelligence model. The research finds that artificial intelligence can flexibly apply foreignization and domestication strategies: when dealing with names of people and places and core science fiction concepts, it tends to use transliteration and literal translation and other foreignization strategy to preserve the uniqueness of the text; when handling idioms, cultural images and complex sentence structures, it tends to use addition, omission, modification and free translation and other domestication strategies to ensure the readability of the translation. However, there are still clear comprehension gaps when dealing with puns, character names, and other texts that contain deep cultural and pragmatic information. The research concludes that current generative artificial intelligence shows significant auxiliary potential for science fiction translation, but it cannot completely replace human translators in cultural decoding and creative rewriting. Human-machine collaboration remains the ideal model for literary translation.

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