With digital learning as the background, this paper employs Python, and other technical means to study the impact of human-computer interaction on student learning behaviors, providing technical support for digital education decision-making. The study uses questionnaire surveys to collect student data on human-computer interaction in the learning process, and utilizes Python to conduct statistical analysis, correlation analysis and regression analysis to explore the impacts of perceived value and flow experience on sustained usage intention, validate the research model, and emphasize the importance of optimizing human-computer interaction and focusing on student needs for successful learning. Technical innovation lies in applying Python and other means to demonstrate the impact of human-computer interaction on student learning behaviors, providing technical support for the digital transformation decision-making in education. Starting from the customer value perspective, this study focuses on improving students’ learning experience, and provides theoretical and practical references for student-centered digital transformation in education.