The task of knowledge tracing is to trace the changes in students’ knowledge state and predict their future performance in learning according to their historical learning records. In recent years, knowledge-tracing models based on attention mechanisms are markedly superior to traditional knowledge-tracing models in both flexibility and prediction performance. Only taking into account exercises involving single concept, most of the existing deep models cannot directly deal with exercises involving multiple concepts, which are, nevertheless, vast in intelligent education systems. In addition, how to improve interpretability is one of the key challenges facing deep knowledge tracing models. To solve the above problems, this study proposes a deep knowledge tracing model based on the embedding off used multiple concepts that considers the relationships among the concepts in exercises involving multiple concepts. Furthermore, the study puts forward two novel embedding methods for multiple concepts and combines educational psychology models with forgetting factors to improve prediction performance and interpretability. Experiments reveal the superiority of the proposed model over existing models in prediction performance on large-scale real datasets and verify the effectiveness of each module of the proposed model.