Metaphor translation plays a key role in cross-cultural communication. Among metaphor types, animal metaphors stand out for their rich cultural connotations and cognitive complexity, making them a valuable testing ground for translation strategies. Despite growing interest, existing research has yet to fully clarify the cultural adaptation mechanisms involved in rendering animal metaphor translation across languages. In particular, how different translation agents dynamically process these culturally loaded expressions remains underexplored, which complicates efforts to optimize human-machine collaboration. This study adopts conceptual metaphor theory and an integrated methodology combining qualitative and quantitative analysis with theoretical interpretation. Drawing on three English translations of Mo Yan’s Frog—by Howard Goldblatt, ChatGPT-4.0, and ChatGLM—this study conducts a systematic comparison of how human and machine translators handle animal metaphors. The analysis shows that effective rendering requires more than literal transfer: it depends on activating culture-specific frames and maintaining evaluative stance, not merely preserving surface imagery. While recent advances in artificial intelligence yield relatively high rates of literal retention, machine translations tend to remain surface-bound when metaphors are culturally or politically charged. By contrast, the cultural awareness and interpretive craft evident in the human translation more consistently preserve metaphorical nuance and ideological force. This study offers new evidence for research on metaphor translation and provides practical guidance for improving human-machine collaborations in literary contexts—e.g., using machine outputs to secure surface mapping while human translators recalibrate cultural frames and stance.