Abstract: Large language models (LLMs) excel in multilingual translation tasks, yet often struggle with culturally and semantically rich Chinese texts. This study introduces the LLM-BT framework, back-translation (BT) powered by LLMs, to evaluate Chinese → intermediate language → Chinese translation quality across five LLMs and three traditional systems. We construct a diverse corpus containing scientific abstracts, historical paradoxes and literary metaphors, reflecting the complexity of Chinese at the lexical and semantic levels. Using our modular NLPMetrics system (including bilingual evaluation understudy [BLEU], character F?score [CHRF], translation edit rate [TER], and semantic similarity [SS]), we find that LLMs outperform traditional tools in cultural and literary tasks. However, the results of this study also uncover a high-dimensional behavioral phenomenon, the paradox of poetic intent, where surface fluency is preserved, but metaphorical or emotional depth is lost. Additionally, some models exhibit verbatim back-translation, suggesting a form of data-driven quasi-self-awareness, particularly under repeated or cross-model evaluation. To address BLEU's limitations for Chinese, we propose a Jieba-segmentation BLEU variant that incorporates word-frequency and n-gram weighting, improving sensitivity to lexical segmentation and term consistency. Supplementary tests show that in certain semantic dimensions, LLM outputs approach the fidelity of human poetic translations, despite lacking a deeper metaphorical intent. Overall, this study reframes traditional fidelity vs. fluency evaluation into a richer, multi-layered analysis of LLM behavior, offering a transparent framework that contributes to Explainable AI (XAI) and identifies new research pathways in cultural natural language processing (NLP) and multilingual LLM alignment.