Drug-drug interaction (DDI) prediction is always a focal point in order to furnish more effective and safer therapy in cases of multiple or complex diseases. We consider this problem from both a functional and a structural solution as drug direct-indirect association information and drug structure information are important and unignorable during this process. Therefore, we propose a method called MVIC (short for multi-view information collaborative fusion for drug-drug interaction prediction) which incorporates drug direct-indirect association and molecular structure information for DDI prediction. MVIC extracts information from two views, i.e., the network view and the molecular structure view. For the former, we construct a drug information network, which then undergoes meta-path-specific representation learning and a designed transformer-like semantic fusion module to obtain the corresponding representations. For the latter, we encode the molecular structure via graph neural network (GNN). In the end, we introduce a multi-view collaborative information fusion module for predicting DDIs. The experiments prove that our method outperforms baselines across all four metrics on three datasets. We also conduct a case study to show the capability of MVIC to predict new prospective DDIs. Our source code is available at https://github.com/scu-kdde/Bioinfo-MVIC-2025 .