In this paper, a knowledge difficulty clustering algorithm (MIBKPC) based on multidimensional time-series data and learning path networks is proposed. The algorithm integrates the cyclic learning sequence, forgetting behavior and system interaction degree formed by learners learning some of the more difficult knowledge points, and constructs a computational model for the difficulty similarity of knowledge points based on system interaction behavior so as to better portray the difficulty similarity of knowledge points based on system interaction behavior. Then a knowledge point difficulty clustering algorithm (MFSKPC) based on multidimensional time-series data and maximum frequent subgraphs is proposed. The algorithm extracts the maximum frequent subgraphs of the atlas based on the learner’s directed learning path atlas using the gSpan-based maximum frequent subgraph mining algorithm and portrays the difficulty similarity of knowledge points based on system interaction behaviors by combining the system interaction degree and the maximum frequent subgraphs. In the accuracy comparison, the clustering accuracy of the MFSKPC algorithm for advanced learners was higher than that of the MIBKPC algorithm for both K=3 and K =5 conditions by 7.89% and 8.29%, respectively. In the comparative analysis of double-loop psychological instruction, the experimental class improved its pre and post-test scores by an average of 2.38 points, while the control group improved by an average of only 1.88 points. The experiment showed that the double-loop teaching based on the MFSKPC algorithm was more effective.