The Application of Graph Neural Network Based on Edge Computing in English Teaching Mode Reform

Abstract

The latest developments in edge computing have paved the way for more efficient data processing especially for simple tasks and lightweight models on the edge of the network, sinking network functions from cloud to edge of the network closer to users. For the reform of English teaching mode, this is also an opportunity to integrate information technology, providing new ideas and new methods for the optimization of English teaching. It improves the efficiency of English reading teaching, stimulates the interest of English learning, enhances students’ autonomous learning ability, and creates favorable conditions for students’ learning and development. This paper designs a MEC-based GNN (GCN-GAN) user preference prediction recommendation model, which can recommend high-quality video or picture text content to the local MEC server based on user browsing history and user preferences. In the experiment, the LFU-LRU joint cache placement strategy used in this article has a cache hit rate of up to 99%. Comparing the GCN-GAN model with other traditional graph neural network models, it performs caching experiments on the Douban English book data and Douban video data sets. The GCN-GAN model has a higher score on the cache task, and the highest speculation accuracy value F1 can reach 86.7.