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This study focuses on the translation and knowledge presentation of Chinese culture in art English textbooks. Due to the complex cultural context and highly specialized terminology in art English textbooks, traditional translation models struggle to accurately convey the deep semantic meaning and artistic value of Chinese culture. This paper proposes a translation enhancement method that integrates an improved Marian neural machine translation (Marian NMT) model with cultural adversarial reasoning networks (Cultural-Adversarial Reasoning Networks). The method employs transfer learning to incorporate Chinese cultural corpora for pre-training and combines a small amount of bilingual annotated data from art textbooks for fine-tuning. The model incorporates a cultural discriminator and generator adversarial mechanism to enhance the identification of culturally loaded words, art terminology, and context, thereby improving the cultural accuracy and educational suitability of the translation. Experiments were conducted on the “Chinese-English Parallel Corpus of Art English Textbooks,” covering themes such as painting, calligraphy, opera, and architecture. The results show that compared to the original Marian NMT, Transformer, and back-translation models, this method achieves significant improvements in BLEU, ROUGE, METEOR, and cultural knowledge integration accuracy (KIA), validating its effectiveness in translating Chinese cultural art English textbooks. The study concludes that this method can enhance the translation quality and teaching presentation effects of Chinese cultural elements in textbooks, providing technical support for the international dissemination of Chinese culture and textbook development.