Given that the traditional model-based collaborative filtering recommendation algorithm fails to effectively utilize the attributes of users and items and the relationship structures among users and items, this study proposes a collaborative filtering recommendation algorithm based onrepresentation learning with graph attention networks. The algorithm uses the knowledge graph to represent the attribute features of the nodes and the relationship structures among the nodes. Then, representation learning of nodes with graph attention networks is performed on the homogeneous networks of users and items to obtain their network embedding feature representations. Finally, a neural matrix factorization model integrating network embedding is constructed to obtain the recommendation results.This paper conducts comparative experiments with related algorithms on the Movielens dataset. Experiments show that the proposed algorithm can optimize the recommendation performance of the model and improve the recommended recall rate HR@K and the normalized discounted cumulative gain NDCG@K.