In the new online learning environment where information technology and education are integrated, it is important to understand the learning patterns of online learners and then predict their learning outcomes to improve learning effectiveness of online learners. In this paper, we collect students’ online learning behavior data through an online learning platform and further investigate the influence mechanism between online learning behavior characteristics and learning performance with the help of the OCCP classification model and the 3D S-F-T model. Meanwhile, the Light GBM algorithm is based on its advantages in noise and distributed processing based on its high accuracy and strong generalization ability in classification problems. The Light GBM algorithm constructs the performance prediction model for the selected learning behavior feature data. The results show that the accuracy of academic achievement prediction based on the Light GBM model is 0.85, and the accuracy of sample prediction classification is H (85%), M (73%) and L (89%), indicating that the model is effective for academic achievement prediction. The experimental results confirm that the prediction method can more accurately identify at-risk students and provide more intuitive and accurate feedback on learners’ problems and deficiencies in the learning process to support the implementation of personalized education interventions.