Application and Optimization of Semantic-enriched Keyword Prefetching Driven by Intelligent Technology in Language Education Network Platforms

Abstract

With the rapid development of intelligent technology, the application of semantically rich keyword prefetching in language education network platforms has gradually become a key technology to improve learning efficiency and user experience. This article proposes a semantic rich keyword prefetching model driven by intelligent technology, aiming to achieve accurate keyword prefetching and recommendation in language education platforms through modeling and optimization. Firstly, based on user behavior data and semantic analysis techniques, a user interest model and a semantic association model were constructed to capture the semantic relationship between users’ learning intentions and keywords. Secondly, by introducing a time decay factor and context aware mechanism, the real-time and accuracy of keyword prefetching have been optimized. Experimental data shows that the MAF1 value, keyword prefetching accuracy and user satisfaction of the model are 0.897, 89.8% and 96%, which are higher than those of compared models, while increasing user satisfaction by 20%. In addition, this article proposes an optimization framework based on A/B testing, which further verifies the robustness and scalability of the model by comparing the effects of different prefetching strategies. The research results indicate that intelligent technology driven semantic rich keyword prefetching can significantly improve the personalized recommendation ability and learning efficiency of language education platforms, providing new ideas for the future development of educational technology.