Exploring drug-drug interactions (DDIs) is crucial for avoiding unknown physicochemical incompatibilities between coadministered drugs. While most studies concentrate on detecting the presence or absence of DDIs, they often overlook the diversity of DDI event types that can significantly enhance drug research and guide scientific drug use. To address this limitation, we propose MNCLDDI, a multi-view nested contrastive learning model designed for the precise prediction of DDI events. MN-CLDDI begins by employing a relational graph convolutional network to capture the various explicit relationships between drugs within a multi-relational DDI graph. This is followed by a transformer framework combined with a convolutional neural network (CNN) to learn the biological features of drugs from their Smiles information. The model then integrates these two feature types into a novel multi-view nested contrastive learning framework, thereby improving the expressiveness of drug embeddings from multiple biological perspectives. Experimental results on two real-world datasets demonstrate that MNCLDDI outperforms state-of-the-art models in predicting DDI events. Moreover, our case studies reveal that considering the multi-view features of drugs simultaneously enables MNCLDDI to predict DDI events with greater accuracy and from a more comprehensive perspective, offering valuable insights into the study of DDI events.