Tracing learners’mastery of knowledge is a pivotal research direction in the realm of wisdom education.Traditional deep knowledge tracing methods predominantly focus on recurrent neural networks，facing challenges such as the lack of interpretability and handling long sequence dependencies.Additionally，many methods overlook the influence of learner characteristics and exercise feature on experimental results.Addressing these issues，a cross-attention mechanism knowledge tracing model was proposed.The model integrated knowledge points and exercise feature information to obtain a question feature embedding module.Subsequently，improvements were made to the attention mechanism based on learner responses，resulting in a dual attention mechanism module.To account for real exercise-solving situations，a guess-error module based on attention mechanism was introduced.firstly，the model took in exercise features information，obtaining a learner response with integrating exercise information through the exercise features embedding module.Following processing by the guess-error module，authentic learner responses were derived.Finally，the prediction module yielded the probability of a learner answering correctly in the next instance.Experimental results demonstrate that the cross-attention knowledge tracing model，incorporating exercise features，outperform the traditional deep knowledge tracing （DKT） model，with 3.13% increase in AUC and 3.44% increase in ACC.This model proves effective in handling long sequence dependencies while exhibiting enhanced interpretability and predictive performance.