Semantic Topic Similarity based Query Method of Web English Education

Abstract

Web English education resources refer to all English teaching information resources with educational value shared on the Internet with digital signals as information. Web English education resources have the characteristics of sharing, intelligence and cross platform. This paper proposes a hierarchical model of Web English education resources pre selection. This model is a two-layer filtering model based on mobile agent. First, it classifies and filters the Web English education resources according to the semantic similarity. Then, according to the Q-learning filtering algorithm in machine learning, the Web English education resources are screened again. This paper designs and develops a prototype system for automatic acquisition of Web English education resources. The experimental results show that: using the algorithm proposed in this paper, we can get the web education resources that users need from the massive Web English education resources, and the quality of user service has been greatly improved.