The prediction and analysis of student performance aims to achieve personalized guidance to students, improve students’ performance and teachers’ teaching effectiveness. Student performance is affected by many factors such as family environment, learning conditions and personal performance. The traditional performance prediction methods either treat all the factors equally, or treat all students equally, which cannot achieve personalized analysis and guidance for students. Therefore, we propose a two-way attention (TWA) based students’ performance prediction model, which can assign different weights to different influence factors, and pay more attention to the important ones. Besides, we also take the individual features of students into account. Firstly, we calculate the attention scores of the attributes on the first-stage performance and the second-stage performance. Then we consider a variety of feature fusion approaches. Finally, we made better predictions of student performance based on the integrated features. We conduct extensive experiments on two public education datasets, and visualize the prediction results. The result shows that the proposed model can predict student performance accurately and have good interpretability.