Cognitive diagnosis is a fundamental task in intelligent education, aiming to accurately assess students’ latent mastery of knowledge concepts. However, existing models typically face two critical challenges. First, they generally treat knowledge concepts as isolated entities, failing to model the pedagogically-grounded causal dependencies among them. Second, as the scale of exercise pools and knowledge systems on online education platforms continues to grow, omissions often occur when annotating exercises with their associated fine-grained knowledge concepts. To address these issues, this paper proposes a causal representation and contextual Q-matrix cognitive diagnosis model (CRQCDM). The model operates through two synergistic mechanisms. First, a causal information-guided representation learning module is used to model the dependencies among knowledge concepts based on a predefined causal graph, generating more interpretable and nuanced student and exercise representations. Second, a contextual Q-matrix enhancement module integrates the student and exercise representations to uncover implicit knowledge concepts associated with the exercises. Extensive experiments were conducted with CRQCDM on three real-world datasets. The results demonstrate that the performance of CRQCDM is superior to that of existing methods.