随着网络课程的增加，在线课程面临着信息过载的问题，着重解决了协同过滤算法在网络课程中的适用问题。 通过分析网络课程中课程的类别及用户的行为，将网络课程进行分类，增加课程的高配合度，并采用基于物品的协同过滤算 法，在计算相似度中根据IUF对用户活跃度进行惩罚，获得较准确的推荐列表。实验结果表明，该算法在网络课程的应用中， 能够提高推荐质量。

With the increase of online courses, online courses are facing the problem of information overload. This article focuses on solving the problem of the application of collaborative filtering algorithms in online courses. By analyzing the types of courses in the online courses and the behaviors of users, the online courses are classified to increase the high degree of cooperation of the courses. An item-based collaborative filtering algorithm is used, and user activity is penalized based on IUF in calculating similarity, so as to obtain a more accurate recommendation list. Experimental results show that the algorithm can improve the recommendation quality in the application of online courses.