**8. Conclusion**

The learning analysis of learning behaviors is a complex process. The data structure, attribute characteristics and relationship categories bring more difficulties. Moreover, the data has strong uncertainty and instability, so it is difficult to achieve technical unity and generality [29]. The development of online learning model gives new definitions and norms to learning behaviors, and also requires new data structure, attribute characteristics, relationship categories, etc. many technologies and methods that can be used in the research of learning behaviors may be inefficient for new data, or do nothing for the new research branches. This research is about the design and application of intelligent data mining technology on a big data set of learning behaviors. Based on Eclat framework, the data structure and algorithms are improved. Starting from the vertical data format, mining probabilistic frequent itemsets, analyzing association rules, and realizing data-driven decision making. In the subsequent research of learning behaviors, for uncertain data, we continue to conduct in-depth research and demonstration of methods and technologies, improve the quality of data analysis and relationship perspective, and provide more valuable conclusions for decision making and prediction feedback of learning behaviors.
