The traditional intellectual property law course cannot adapt to the needs of cultivating students’ active learning ability, practical ability and innovation ability in the digital era due to the teaching concept, teaching means, teaching method and assessment form. This study proposes an information-based teaching model, which builds a course knowledge organization model using a formal expression language, through atomized knowledge points and knowledge granularity. The k-means algorithm has been improved from the perspective of center selection and constraint rules to adapt to the characteristics of the intellectual property law course and complete the clustering and extraction of knowledge points. The frequent item-based method analyzes the students’ answers and generates the corresponding association rules to further develop the analysis of learning situations. For a college law school study, it was found that 925 simultaneous wrong answers and 8152 simultaneous correct answers were successfully mined using clustering. In applying this college students’ evaluation scores of this information technology teaching in terms of content design, presentation design, structural design and relationship design of knowledge clustering are 0.796, 0.818, 0.788, 0.794, and overall good results are achieved.