FCM Clustering on Interaction Pattern Analysis of Chinese Language Learner Behavior

In order to meet the needs of the current personalized education, improve the shortcomings of the current digital learning system in personalized learning, and introduce the service concept into education, the development and research of the personalized learning system based on the analysis of user behavior patterns has become the development of digital learning. This paper studies a language learning method, that includes obtaining the user's language learning interaction data to determine the user's language level: wherein, the user's language level data includes the user's initial language level and the current language level; initial learning model: according to the current language level, an adaptive algorithm is used to update the initial learning model, and the user performs linguistics further according to the updated learning model. In addition, in order to realize the clustering analysis of Chinese language online learning users' learning behavior, since the Fuzzy C-means (FCM) clustering results are easily affected by the selection of their initial cluster centers, a Harmony Search (HS)-FCM-based Chinese language learning user learning behavior clustering analysis is proposed. The participation dimension, focus dimension, regularity dimension, interaction dimension, and academic performance are selected as the analysis indicators of learning behavior. The learner level is divided into 5 levels, namely excellent, good, medium, qualified, and poor. Compared with HSFCM, and decision tree, it is found that the algorithm Improved HS (HIS)-FCM in this paper has higher clustering accuracy, faster convergence speed, and lower fitness, which provides new opportunities for learner level division and optimization of course learning.