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BKT can track the state of the learners ' knowledge, as well as predict their mastery level and future performance in the intelligent teaching system. Because BKT easily ignores the phenomenon of memory forgetting, and does not consider the impact of learning behavior on performance results, the model prediction results deviate from the actual situation. So this paper proposed a new Bayesian knowledge tracking model that integrated learner behavior and forgetting factors. Firstly, the method used the decision tree algorithm to process learning behavior data and introduced behavior nodes. Then, the model initialized the forgetting parameters and assigned them, and updated the algorithm of the learner ' s knowledge mastery level. Finally, the experiment compared the prediction accuracy of related models through the public data set provided by ASSISTMENTS. Experimental results show that the BF-BKT model can achieve better prediction accuracy. [ABSTRACT FROM AUTHOR]