Machine learning-based analysis of online course learning experience

In order to timely and accurately reflect the learning experience in online learning and improve the course quality of online learning platform, the author selected the representative icourse163.org as the research object, and proposed a method based on machine learning for analyzing online course evaluation text. This method uses Scrapy framework of web crawler, Jieba Chinese word segmentation tool, Word2vec word vector conversion model, LDA topic model and other research tools, and uses TF-IDF algorithm. This analysis illustrate some problems existing in icourse163.org from three aspects of TF-IDF characteristic word frequency analysis, topic-keyword matrix and the proportion of negative evaluation topics. The results prove that the method proposed in this paper is of practical value. This method is expected to contribute to the development of online education.