Data.

To more accurately capture the expression of the English humanistic landscape in agricultural industrial parks under the emerging agricultural paradigm of fish-vegetable symbiosis, and to address the limitations of unscientific evaluation standards and inadequate adaptability in Chinese-English translation within multimodal contexts, this study proposes an intelligent translation evaluation framework based on the SPEAKING model—comprising Setting, Participants, Ends, Act Sequence, Key, Instrumentalities, Norms, and Genre. The study identifies the core elements essential for articulating the English humanistic landscape of agricultural industrial parks and conducts a comprehensive analysis from the dual perspectives of translation accuracy and adaptability. Fish-vegetable symbiosis, an ecological agricultural system integrating aquaculture and plant cultivation, emphasizes resource recycling and ecological synergy. Internationally referred to as the “aquaponics system,” this model has become a pivotal direction in sustainable ecological agriculture due to its efficiency and environmental compatibility. This study investigates multimodal translation tasks across text, image, and speech data. It addresses two primary challenges: (1) the absence of robust theoretical grounding in existing translation evaluation systems, which leads to partial and insufficiently contextualized assessments in agricultural industrial park translations; and (2) difficulties in maintaining consistency and readability across multimodal translation tasks, particularly in speech and visual modalities. The proposed optimization model integrates linguistic theory with deep learning techniques, providing a detailed analysis of contextual translation elements. Comparative evaluations are conducted against five prominent translation models: Multilingual T5 (mT5), Multilingual Bidirectional and Auto-Regressive Transformers (mBART), Delta Language Model (DeltaLM), Many-to-Many Multilingual Translation Model-100 (M2M-100), and Marian Machine Translation (MarianMT). Experimental results indicate that the proposed model outperforms existing benchmarks across multiple evaluation metrics. For translation accuracy, the Setting score for text data reaches 96.72, exceeding mT5’s 92.35; the Instrumentalities score for image data is 96.11, outperforming DeltaLM’s 93.12; and the Ends score for speech data achieves 94.83, surpassing MarianMT’s 91.67. In terms of translation adaptability, the Genre score for text data is 96.41, compared to mT5’s 93.21; the Key score for image data is 92.78, slightly higher than mBART’s 92.12; and the Norms score for speech data is 91.78, exceeding DeltaLM’s 90.23. These findings offer both theoretical insights and practical implications for enhancing multimodal translation evaluation systems and optimizing cross-modal translation tasks. The proposed model significantly contributes to improving the accuracy and adaptability of language expression in the context of agricultural landscapes, advancing research in intelligent translation and natural language processing.