With the online development of computer science education, effectively integrating online course resources has become a key challenge to improve teaching quality. A model based on Bayesian classification is researched and constructed to realize the optimization and integration of online course resources for computer majors. By analyzing students' learning behavior data and combining with the dynamic characteristics of teaching resources, students' preferences for different resources are predicted, and personalized resource recommendations are provided accordingly. Through this method, students' learning experience is enhanced, resource utilization efficiency is improved, and learning outcomes are ultimately promoted, providing theoretical support for teaching resource management in the field of online computer science education.
维普
万方数据