Drug-Target Interaction (DTI) prediction is an indispensable process in drug repositioning. Wet-lab experiments for potential DTI identification are reliable but expensive, labor-intensive, and time-consuming. Deep learning demonstrates the superior representation learning capability in the DTI prediction. However, there is still debate about how to accurately learn drug and protein features and further effectively fuse these features. To address the above issues, this work introduces SGcCA, an end-to-end DTI prediction framework by incorporating Spatial and Channel reconstruction Convolution (SCConv), Graph convolutional Network (GCN), and Cross-efficient-additive Attention (CEAA). First, an SCConv module is proposed to encode drug features from their SMILES strings and protein features from their amino acid sequences by reducing spatial and channel redundancies. Next, GCN is employed to encode drug features from their 2D molecular graphs. Subsequently, a CEAA block is devised to fuse the learned drug and protein features. Finally, the fused features are taken as the inputs and all unobserved drug-target pairs are classified through a multilayer perceptron. Using accuracy, F1-score, MCC, AUROC, and AUPRC as evaluation metrics, SGcCA outperformed six popular DTI prediction models (i.e., CPI-GNN, MolTrans, BACPI, CPGL, GIFDTI, and FOTF-CPI) under four different experimental scenarios on four publicly available DTI data sets (Human, C.elegans, BindingDB, and DrugBank), showcasing its better interpretability and generalization ability. Ablation study further underscored the importance of SCConv, CEAA, and GCN. Moreover, visualization of the fused features along with case study and molecular docking outcomes ensured that the predicted DTIs matched closely with the real interactions, further proving the greater performance of SGcCA. As an open-source tool, SGcCA is poised to provide support for drug repositioning. The source codes and data are freely available: https://github.com/plhhnu/SGcCA.