Research on Classification Method of Answering Questions in Network Classroom Based on Natural Language Processing Technology

Abstract

In order to overcome the inaccuracy of the current research results of online classroom question-answering classification, a method of online classroom question-answering classification based on natural language processing technology is proposed. The entity relationship model of the network classroom question answering system is constructed, and the model is transformed into the relational data model, the network classroom question answering database is constructed. TF-IDF technology is used to extract curriculum keywords, construct attribute word set, use natural language processing technology to segment students' questions reasonably in the network classroom, convert the words into vectors, calculate the question similarity according to cosine theorem, and then return the answers with the highest degree of similarity to students in the same type of questions. Experimental results show that the classification accuracy of the proposed method is always above 96%, and the user satisfaction is above 94%, with high classification accuracy and user satisfaction.