Query Method of Web Education Resources Based on Semantic Topic Similarity

Abstract

This paper researched on the query method of web education resources based on semantic topic similarity. This paper proposes a hierarchical model of web Education. This model is a two-tier filtering model based on mobile agent. The model first classifies and filters according to semantic similarity, and then filters web education resources according to Q-learning filtering algorithm in machine learning. This paper presents a kind of rank algorithm for web educational resources. According to Euclid ambiguity and RSS aggregation technology, this algorithm finds the web education resources that users need. First of all, RSS document aggregation technology is used to quickly gather the music education resources that users need. Secondly, the query is characterized by Euclid fuzziness in fuzzy sets. The fuzziness of the association between content and resources. Finally, we get the personalized resources to satisfy the end users of web education resources.