基于模糊聚类的在线数学课程智能匹配算法设计与仿真

Design and simulation of online mathematics course intelligent matching algorithm based on fuzzy clustering

针对在线教育平台中大量数学课程资源利用率偏低、资源利用不符合教学逻辑等问题,文中基于模糊聚类算法提出匹配数学课程资源的智能化算法。通过深入分析常用智能匹配算法的主要原理,详细讨论多种常用匹配算法的优点和缺点。在此基础上,利用传统模糊聚类算法和模糊C均值聚类算法的思想,改进基于用户的协同过滤匹配算法,从而降低匹配算法中的数据稀疏性。仿真测试结果表明,在数学课程资源的匹配效果方面,与传统的协同过滤匹配算法相比,基于模糊C均值聚类的智能匹配算法具有更高的推荐准确度,可以有效提升教学效果。

In allusion to the problems of low resource utilization of a large number of mathematics courses in online education platforms and resource utilization inconsistent with teaching logic,an intelligent algorithm that matches mathematics course resources is proposed on the basis of fuzzy clustering algorithm.The main principles of commonly used smart matching algorithms are analyzed deeply,and the advantages and disadvantages of many commonly used matching algorithms are discussed in detail.On this basis,the thoughts of the traditional fuzzy clustering algorithm and fuzzy C⁃means clustering algorithm are utilized to improve the user⁃based collaborative filtering matching algorithm for reducing the data sparsity in the matching algorithm.The simulation test results show that,in comparison with the traditional collaborative filtering matching algorithm,the intelligent matching algorithm based on fuzzy C⁃means clustering has higher recommendation accuracy and can improve the teaching effect more effectively in terms of the matching effect of mathematics course resources.