Abstract: Knowledge tracing is a new field combining artificial intelligence technology and education. It aims to evaluate the knowledge state of students through the interactive sequence of exercises completed by students in the past. It is a key core technology to realize large-scale personalized learning services. With the wide application of deep learning in computer vision, natural language processing, recommendation system and other fields, a large number of neural network-based methods, referred to as deep knowledge tracing model, have emerged in the field of knowledge tracing. Aiming at the shortcomings of the existing deep knowledge tracing models in terms of interpretability and accuracy, this paper proposes a knowledge tracing model SLKT that integrates sequence feature and learning process, including knowledge state module, sequence feature module and prediction module. The knowledge state module is used to simulate the learning process of students, and the sequence feature module captures the recent learning status of learners. Through the integration of sequence features and learning process, SLKT model effectively solves the problem that the method based on knowledge state modeling cannot consider learners' recent learning status. Meanwhile, a dynamic Q-matrix with constraints is proposed to represent the relationship between exercises and knowledge points, so as to better model learners' learning process. It can ensure better interpretability and improve the accuracy of the model effectively. In this paper, the superior performance of the proposed model in the evaluation of AUC and ACC indicators is verified by comparing multiple sets of experiments with depth tracing models such as DKT, DKVMN, SAKT and CKT on the public data sets in three knowledge tracing fields.