Focusing on the prediction of student grade in the undergraduate teaching of higher education， a prediction algorithm based on course Knowledge Graph （KG） was proposed. Firstly， a course KG representing course information was constructed. Then， the neighbor-based methods and the KG representation learning-based methods were used to calculate the similarity of the courses on the knowledge level based on the KG， and those knowledge similarities among courses were integrated into the traditional grade prediction framework Collaborative Filtering （CF）. Finally， the performance of the algorithm with fusing KG and the common prediction algorithm in different data sparsities were compared in experiments. Experimental results show that in the data sparse scenario， compared with the traditional CF algorithm， the neighbor-based algorithm has the Root Mean Square Error （RMSE） reduced by about 11% and the Mean Absolute Error （MAE） reduced by about 9%； and compared with the traditional CF algorithm， KG representation learning-based algorithm has the RMSE reduced by about 17.55% and the MAE reduced by about 11.40%. Experimental results indicate that the CF algorithm using KG can significantly reduce the prediction error， which proves that the KG can be used as information supplement in the lack of historical data， thus helping CF to obtain better prediction results.