Design and practice of university English multimodal intelligent interaction system for translation learning

With the rapid development of computer and network technology, multimodal discourse is spreading and used more and more frequently. Currently, there are problems such as single learning mode, insufficient interactivity and weak personalized learning support in the process of university English translation learning. In order to solve these problems, this paper proposes a multimodal intelligent interaction system for university English translation learning. Based on multimodal data fusion technology, the system organically combines text, voice, image and other forms of data to build an intelligent interaction platform, which realizes instant feedback, personalized learning path recommendation and efficient translation learning support. By designing core functional modules, including multimodal data processing, intelligent interaction mechanism and personalized learning recommendation algorithm, this paper completes the overall architecture and functional design of the system. The system has significant advantages in enhancing the efficiency of translation learning and optimizing the learning experience, and at the same time, it provides new ideas for the intelligent transformation of university English teaching mode.