Recently, recommending bundles - sets of items that complement each other - instead of individual items to users has drawn much attention in both academia and industry. However, previous GNN-based methods fail to explicitly model intricate ternary relationships. Additionally, the loose combination of node embeddings from different graphs tends to introduce noise, as it fails to consider disparities among user preferences. To this end, we propose a novel approach called AMCBR. Specifically, AMCBR models ternary interactions by constructing multiple graphs. Then, multi-graph convolution is applied to each graph to encode diverse potential preferences. To enhance the model’s robustness, an adaptive aggregation module is employed to assign varying weights to node embeddings from different graphs during the fusion process, which enriches the semantic and comprehensive information in the embeddings while mitigating potential noise. Finally, a contrastive learning strategy is proposed to jointly optimize the model, strengthening collaborative links between individual graphs. Extensive experiments on two real datasets demonstrate that AMCBR can outperform the state-of-the-art baselines on the Top-K recommendations.