In order to improve the accuracy of personalized recommendation of traditional Chinese medicine remote teaching resources, this article proposes a personalized recommendation method for traditional Chinese medicine remote teaching resources based on data mining. Firstly, based on the preference values of the attribute features of remote learning resources, the attribute features of remote learning resources are predicted and graded, and association rules are used to extract the attribute features from the remote learning resources. Secondly, using the keywords searched by learners as user interest features, the similarity between user interests and remote learning resources is obtained, and the scoring features of different remote learning resources are calculated. Finally, the similarity matrix of remote learning resources is used to predict users’ ratings of remote learning resources. By introducing users’ preferences for remote learning resources, the similarity between remote learning resources is calculated, and collaborative filtering of remote learning resources is completed to achieve personalized recommendation of traditional Chinese medicine remote teaching resources. The experimental results show that the personalized recommendation of this method can not only improve the coverage and accuracy, but also reduce the Mean absolute error and Root-mean-square deviation of the recommendation.