The study of student behavior data is a necessary way to enhance the digitalization process of the curriculum system of business administration. This paper collects students’ online platform learning behavior and other data, and carries out data cleaning and other pre-processing on them. Using the density function and entropy discretization algorithm to divide the continuous student data into intervals, and study the course learning characteristics of students in different attribute intervals. On this basis, optimize the curriculum system of business administration majors in colleges and universities, and judge its application value through comparative experiments. Obtain students’ satisfaction data on the curriculum system of business administration majors in colleges and universities, and analyze the direction of continuous improvement. Through preprocessing and descriptive analysis, it can be judged that the student behavior data conforms to the characteristics of continuous data, and can be classified using the discretization algorithm. The student behavior data are discretized into 3 major categories and 11 subcategories, and the attribute characteristics of each category can represent the behavioral characteristics and curriculum needs of different students. The mean value of the overall satisfaction of the optimized business administration curriculum system is 3.567 points, and the scores of all dimensions are higher than those before optimization, and there is no gender difference in satisfaction (P>0.05). The entropy-based discretization algorithm can effectively support the optimization of business administration professional course system.