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With the greater popularity of the Internet, on the one hand, online education platforms have flourished and education informatization has entered a new era, with massive learning behavior data generated by learners in different learning platforms. This paper constructs a portrait of student learning behavior based on the characteristics of their learning behavior in the health education class. Based on the clustering analysis method in the big data mining algorithm, the user’s behavioral characteristics are analyzed. On this basis, the algorithm is improved by using collaborative filtering personalized recommendation and introducing the LDA model, and a customized teaching model is constructed in the context of health education, and its application effect is explored. From the four dimensions of “course completion characteristics”, “teaching interaction characteristics”, “learning input characteristics,” and “learning achievement characteristics”, we analyzed different groups of students and investigated the effect of its application. “The behavioral characteristics of different groups of students were analyzed, and it was concluded that Group A and Group B performed better, but Group C accounted for a higher percentage of 24%. Finally, according to the collaborative filtering-based digital customized teaching on the students’ health education test scores for the pre and post-test, it was concluded that the average scores of the experimental subjects increased after the post-test. The mean scores for the third post-test were 4.27, 4.44, and 4.35, respectively. It can be concluded that the customized teaching model has a significant improvement in the students’ classroom teaching effectiveness.

In the process of identifying unknown protocol of bit stream, the clustering of data sets of bit stream in the protocol is the basis of further identifying unknown protocol. Therefore, on the one hand, this paper analyses the classical clustering algorithms used in unknown protocol recognition from three perspectives: the whole process of clustering analysis, similarity measurement and clustering result evaluation. On the other hand, the development trend of clustering algorithm in unknown protocol recognition is summarized, and other problems in unknown protocol recognition can be solved by clustering algorithm according to the characteristics of bit stream data set, which can provide reference for future research work. Finally, the challenges faced by the Research Institute and the prospects for future work are given.