Exploring drug-target interactions (DTIs) is crucial for drug discovery. Most existing methods for predicting DTIs rely solely on the linear structures of molecules, such as SMILES or the amino acid sequence. However, these linear features fail to reflect the substructures of molecules or the relative positions of atoms. The 2D molecular structures, such as skeletal formulas or atom graphs, also have limitations in fully reflecting the chemical structure of molecules. To fully leverage the chemical structure of molecules, this paper proposes DCGCN, a DTI prediction method based on 3D molecular structure. DCGCN decomposes the 3D point cloud data of a molecule into three components: atomic sequence, atomic connectivity, and a distance map. From its connectivity and distance information, DCGCN captures the relationships among atoms through a dual-channel graph convolutional network. Furthermore, 1D convolutional layers are employed to extract the chemical components with sequence information. Experimental results on two public data sets demonstrate that DCGCN outperforms several state-of-the-art DTI prediction methods, indicating that incorporating the 3D structures of molecules can significantly improve DTI identification.