Mainstream knowledge graph-based recommendation model rarely consider the relationship between source nodes and target nodes when fusing high-order information, leading to the introduction of too much noise information and thus affec-ting recommendation performance in complex network scenarios. To address this problem, this paper proposed a knowledge graph recommendation model with integrated meta-graph neighborhoods, with the goal of reducing the impact of noise information by constructing and integrating meta-graph neighborhoods, thereby improving recommendation performance. Firstly, the model obtained the initial similar sequence of the source node based on meta-graph similarity. Then, the model enhanced the initial sequence using self-attention networks and linear networks, which resulted in a set of enhanced feature vectors that serve as the meta-graph neighborhoods of the node. Secondly, the model designed an attention mechanism based on the user's different preferences for each meta-graph to perform convolution and aggregation on the resulting meta-graph neighborhoods. Then, the model integrated the meta-graph neighborhoods into the source node to enhance the feature representation of the source node. Finally, the model used the inner product of the enhanced vector and the user vector as the probability of user interaction with the item, which was then utilized to complete the recommendation. Experimental results on the MovieLens-20M and Last-FM datasets show that the proposed model achieves an AUC of 97.3% and 94.3%, and F₁-score of 83.1% and 75.6%, respectively. The recall@50 are 35.4% and 31.7%, respectively. These performance metrics outperform models such as NGCF, KGCN, LKGR, and other models. The results demonstrate that the knowledge graph recommendation model with integrated meta-graph neighborhoods is effective in improving recommendation performance.