Recommender systems based on collaborative filtering (CF) have been a prominent area of research. In recent years, graph neural networks (GNN) based CF models have effectively addressed the limitations of nonlinearity and higher-order feature interactions in traditional recommendation methods, such as matrix decomposition-based methods and factorization machine approaches, achieving excellent recommendation performance. However, existing GNN-based CF models still have two problems that affect performance improvement. First, although distinguishing between inner interaction and cross interaction, these models still aggregate all attributes indiscriminately. Second, the models do not exploit higher-order interaction information. To address the problems above, this article proposes a collaborative filtering method based on GNN with attribute fusion and broad attention, named GNN-A