Aiming at the problem that the traditional performance prediction method does not treat the importance of each attribute feature to student’s scores and the low completion rate of student’s online learning differently, a convolutional neural network of relational graph and gated recurrent unit integrated into the attention mechanism（AR-GCN-GRU）score prediction method for students is proposed.The integrated attention mechanism is used to capture the relationship attribute characteristics between students, and at the same time, extract the important attribute characteristics of students and visualize them,And the method integrates the advantages of the convolutional neural network of relational graph (R-GCN) and the Gated recurrent Unit (GRU),and can not only capture the internal correlation between nodes,but also extract the representative information of students' behavioral attributes well.The model was contrasted and ablation experiment on a public data set,The F value and accuracy of the model reached 99.00% and 99.73%, The experimental results show that the method has been significantly improved than other algorithms, and the effectiveness of the attention mechanism is verified.