Title: Text classification and topic mining of intelligent forum comments in university MOOC based on CNN networks

Authors: Xue'e Zhong

Addresses: College of Marxism, Geely University of China, Chengdu, 641423, China

Abstract: This study conducted an intelligent classification of comments on MOOC forums, categorising texts based on urgency and subject. In emergency level classification, LSTM recurrent neural network is used, and after identifying emergency comments, Bayesian subject mining models and CNN networks are used to perform secondary text classification. In the emergency classification simulation experiment, the RNN-LSTM model proposed in this study has three evaluation indicators: overall accuracy, recall rate, and F1 value, which are 0.91, 0.949, and 0.94, respectively, which are higher than conventional classification methods. In the practical application scenario of text mining, the probability of misjudgement of Bayesian network combined with CNN model is 4%. Research has shown that the MOOC forum intelligent management comment classification method proposed in this study can effectively increase the feedback efficiency of teachers in MOOC forum comment management, and improve the feedback communication effect of teacher-student education communication.

Keywords: recurrent neural network; RNN; LSTM; Bayesian networks; CNN; Catechism forum.

DOI: 10.1504/IJCSYSE.2025.145005

International Journal of Computational Systems Engineering, 2025 Vol.9 No.1, pp.58 - 67

Received: 10 Apr 2023
Accepted: 24 Sep 2023
Published online: 17 Mar 2025 *