To provide intelligent learning guidance for students in e-learning systems, it is necessary to accurately predict their performance in future exams by analyzing score data in past exams. However, existing research has not addressed the uncertain and dynamic features of students' cognitive status, whereas these features are essential for improving the accuracy of performance prediction. A novel approach via neutrosophic cognitive diagnosis is proposed to predict student performance in a personalized e-learning environment. This approach innovatively employs the neutrosophic set (NS) theory to comprehensively measure the students' cognitive status on knowledge concepts from three features, i.e., understanding level, degree of misunderstanding, and uncertainty. A new measurement method of NS similarity is used to identify top-k neighboring students for one student. Then, we predict a student's performance on exercises in future exams by integrating a neighborhood-based collaborative filtering algorithm with a probabilistic matrix factorization method to address the data sparsity problem. Finally, the experiments based on real-world datasets demonstrate that the proposed approach provides higher prediction accuracy than the existing ones with a low execution cost.