Drug-drug interaction (DDI) refers to the interaction between two or more drugs in the body, altering their efficacy or pharmacokinetics. Fully considering and accurately predicting DDI has become an indispensable part of ensuring safe medication for patients. In recent years, many deep learning-based methods have been proposed to predict DDI. However, most existing computational models tend to oversimplify the fusion of drug structural and topological information, often relying on methods such as splicing or weighted summation, which fail to adequately capture the potential complementarity between structural and topological features. This loss of information may lead to models that do not fully leverage these features, thus limiting their performance in DDI prediction. To address these challenges, we propose a relation-aware cross adversarial network for predicting DDI, named RCAN-DDI, which combines a relationship-aware structure feature learning module and a topological feature learning module based on DDI networks to capture multimodal features of drugs. To explore the correlations and complementarities among different information sources, the cross-adversarial network is introduced to fully integrate features from various modalities, enhancing the predictive performance of the model. The experimental results demonstrate that the RCAN-DDI method outperforms other methods. Even in cases of labelled DDI scarcity, the method exhibits good robustness in the DDI prediction task. Furthermore, the effectiveness of the cross-adversarial module is validated through ablation experiments, demonstrating its superiority in learning multimodal complementary information.