Abstract: Aiming at the problem that the existing deep knowledge tracking model lacks comprehensive consideration of exercises and student characteristic information. A deep knowledge tracking model by integrating problem difficulty and answering experience (DKT-DE) is proposed. The model evaluates the difficulty of the exercises and the students' answering experience by analyzing the answer sequence to enrich the feature information of the input layer of the model, thereby improving the prediction performance of the model. Finally, the DKT-DE model was compared with five baseline models(BKT, DKT, DKT+, DKVMN, SAKT model) through comparative experiments and ablation experiments on three public datasets(ASSISTments2009, ASSISTments2015, ASSISTments2017). The comparative experiment results demonstrate that the DKT-DE model can more accurately assess students' knowledge mastery. Compared with the best-performing baseline model, DKT+, the DKT-DE model achieves an average AUC improvement of 2.78%, 2.44%, and 1.5% on the ASSISTments2009, ASSISTments2015, and ASSISTments2017 datasets, respectively. The ablation experiment results further confirm the positive contributions of incorporating question difficulty and student response experience to enhance the predictive capability of the model.