The existing knowledge concept recommendation system does not consider the short-term interest of users. To solve the problem， a Knowledge Concept Recommendation system based on Interest Enhancement （KCRec-IE） was proposed. Firstly， users’ short-term interests were captured according to the users’ knowledge concept click sequences， and a heterogeneous graph was constructed by using the side information. Then， the representation learning of knowledge concept entities and user entities was carried out on heterogeneous graph by using meta-path-guided graph convolution. Different from the representation learning of knowledge concept entities， when learning the representation of user entities， the contributions of different neighbor users to target users were able to be distinguished according to the short-term interests of users. Finally， the score prediction was realized according to the knowledge concept entities， the user entities and the user’s short-term interests. Experimental results on public dataset Xuetang X show that compared with KCRec-SEIGNN， KCRec-IE is improved by 3.60 percentage points on HR@5； compared with KCRec-IE_n， KCRec-IE is improved by 1.02 percentage points on HR@10； compared with KCRec-SEIGNN， KCRec-IE is improved by 1.60 and 1.18 percentage points respectively on NDGC@5 and NDGC@10 respectively， verifying the effectiveness of the proposed method.