For the problem of poor objective evaluation of online education courses, a prediction model based on Light Gradient Boosting Machine - Field-aware Factorization Machine - Logistic regression is designed. The model collects online course viewing history data, extracts users' generic features, temporal features and other feature values, and focuses on the relationship between high-dimensional features and low-dimensional features of feature values to achieve multi-dimensional feature combinations and improve data sparsity, so as to improve rating prediction performance. Based on masked data test on an online course website, the determination coefficient between predicted grading and actual one in this model is 0.87, with 0.42 of average mean square error, improved model generalization capability, this model serves more objective and realistic predicted grading to online course.