The proficiency of students’ knowledge points is an important basis for teachers to make learning plans. To tackle the problem that the students’ proficiency for knowledge points cannot be described in a probabilistic way in cognitive diagnosis, this study proposes a prediction method of embedding knowledge points as features. This method establishes knowledge point vectors for students and test questions respectively and constructs a convolutional neural network for supervised learning to adjust students’ proficiency for knowledge points according to their answering records. Compared with existing related methods, the proposed method greatly improves the accuracy.