Cognitive diagnosis is a fundamental problem in intelligent education systems,which aims to evaluate the mastery le-vels of students on different knowledge concepts.Although the performance current deep learning-based cognitive diagnostic me-thods has improved greatly compared with traditional methods,they cannot fully exploit the potential correlation between concepts.To this end,this paper proposes an attention-based concept enhanced cognitive diagnosis(ACECD) model to obtain more accurate cognitive diagnostic results by modeling the relationship between related concepts.Specifically,we first project students,exercises,and concepts to factor vectors to perform complex interactions,and then feed the concept factors into a self-attention network to capture the implicit correlations that exist between concepts,and concept factor vector can be enhanced with the captured implicit relation.Finally,the enhanced concept factors are interacted with the student factor and the practice factor,and the interacted results are input into the diagnosis module to get the final diagnosis result.In addition,we also use the interaction between the practice factor and the concept factor to correct the bias of the manually-labeled Q matrix.The proposed model is compared with other methods on two real-world datasets,and the experimental results show that the ACECD model effectively improves the diagnostic results.