Emotion Recognition in Chinese-English Translation Based on Deep Learning in Intercultural Communication

In the context of globalization, intercultural communication has become increasingly frequent, and Chinese-English translation plays a crucial role in this process. This study focuses on applying deep learning-based emotion recognition to Chinese-English translation in intercultural communication. An innovative Enhanced Conformer model is proposed, which combines a one-dimensional text CNN and the BERT model. Through a dual-path structure and a specific fusion mechanism, the model comprehensively captures emotional and semantic features of the text. Experiments are conducted using a constructed Chinese-English parallel corpus, and the results show that the proposed model significantly improves emotion recognition accuracy and translation quality compared to traditional models. This research not only enriches the theoretical research in the field of translation but also provides practical solutions for enhancing cross-cultural communication through accurate translation.