Data sparsity occurs in recommendation systems and the cold-start problem exists in newly launched items due to a lack of user interaction data when providing targeted user interest recommendations. To address these problems, this study proposes a user interest recommendation algorithm based on knowledge graphs. First, to tackle the data sparsity issue in users’ potential interests, it employs a multi-layer graph neural network (GNN) to capture the direct, indirect, and deeper relationships between users and items through their embedding vectors. Second, for users’ explicit interests, it introduces a graph structure enhancement technique to randomly delete explicit relationships between users and items based on rating weights. This method leverages an encoder to analyze the relationships of new users and item nodes, uncovering interactive relationships between users and items, thereby addressing the cold-start problem. Finally, a feature cross-compression module is used to combine knowledge graph embeddings with the recommendation task to achieve feature sharing. The shared features further deepen the interaction between items and knowledge graph entities, enhancing recommendation accuracy. Experiments conducted on the Book-Crossing and Last.FM datasets demonstrate that the proposed algorithm significantly outperforms other baseline algorithms in terms of AUC and ACC indicators.