Drug-drug interactions (DDIs) refer to pharmacological and clinical responses to a drug combination, which are different from the known mode of actions of two drugs when used alone. Identifying potential DDIs is helpful for studying combination therapies and avoiding adverse effects that may occur when multiple drugs are used together. A number of models have been proposed to predict DDIs. However, identifying drug features and combining those features from multiple sources are still challenging. In this study, we propose a deep learning framework to identify potential DDIs, generate drug features from molecular view and DDI graph view, and fuse them together adaptively. In the molecular view, we take the atom attributes and molecular graphs into account, to reflect the chemical and topological properties of a drug molecule. In the DDI graph view, we concatenate those features from both chemical substructures and large language model, and design a soft-threshold dimensionality reduction network to retain essential features. In the feature fusion process, we design two adaptive parameters to concatenate those multi-view features. To systematically assess the efficacy of our proposed methodology, we performed comprehensive empirical evaluations across two benchmark datasets, employing comparative analysis against five contemporary cutting-edge approaches. The consistent experimental outcomes across all test scenarios substantiate the superior performance characteristics of our novel framework. In case studies, it shows the application value under realistic conditions.