Application of Deep Learning Algorithms in the Design of Interactive Chinese Corpus System

With the rapid growth of AI technology, deep learning algorithms have made remarkable achievements in the field of natural language processing, especially showing great potential in processing Chinese, the world’s most widely spoken language. The design and growth of Chinese corpus systems are of vital importance to promoting Chinese teaching, research and application. However, traditional corpus systems lack interactivity and are unable to meet users’ urgent needs for instant feedback and personalized learning. To address this challenge, this paper explores the application of deep learning algorithms in the design of an interactive Chinese corpus system to improve the system’s interactive performance and user experience. This paper first outlines the basic concepts of deep learning algorithms and interactive corpus systems, and then analyzes the limitations and shortcomings of current research. On this basis, this paper elaborates in detail the design logic, implementation path and experimental verification results of the proposed interactive Chinese corpus system based on deep learning. Experimental data show that the system can greatly improve the user’s interactive operation experience in the process of learning Chinese, and its operation time is between 0-2s, bringing users a more efficient new learning experience. At the same time, this paper also conducts an in-depth discussion of the experimental results, points out the limitations of the system, and proposes future improvement strategies and growth directions.