Given the uneven modeling degree between the user and project sides of the recommendation algorithms for knowledge graphs as well as high model complexity, a recommendation algorithm that integrates knowledge graph and lightweight graph convolutional network is proposed. On the user side, neighbor sets are generated based on user similarity, and the interaction records of users and their similar users are iteratively propagated on the knowledge graph for many times to enhance the representation of user features. On the project side, the entity on the knowledge graph is embedded and propagated to mine the project information related to user preferences. Then, the lightweight graph convolutional network is adopted to aggregate neighborhood features to obtain the feature representations of users and projects. At the same time, the attention mechanism is employed to incorporate neighborhood weights into the entities to enhance node embedding representation. Finally, the ratings between the user and the project are predicted. Experiments show that on the Book-Crossing dataset, compared with the optimal baseline, AUC and ACC are improved by 1.8% and 2.3%, respectively. On the Yelp2018 dataset, AUC and ACC are improved by 1.2% and 1.4%, respectively. The results demonstrate that the proposed model has better recommendation performance compared with other benchmark models.