The application of deep learning to seismic fault interpretation is often constrained by computational costs. To address this, we propose a novel strategy that decouples the context window size (the spatial range of seismic observations) from the input resolution (the actual matrix dimensions fed into the model), systematically investigating their combined impact on computational efficiency and interpretation accuracy. Using field-acquired seismic data from two distinct coal mines, we trained a lightweight two-dimensional convolutional neural network (CNN) on samples extracted with varying context windows (8 × 8 to 64 × 64 pixels), which are then uniformly resized to a fixed low resolution of 8 × 8 pixels. Our results demonstrate that enlarging the context window consistently improved model performance, with the 64 × 64 window achieving the highest precision (99.46%) and fault continuity, even after downscaling. In contrast, a combined multi-scale training set did not outperform the best single-window model, indicating that effective multi-scale fusion requires more advanced architectural designs. Our workflow highlights that contextual information remains crucial for feature learning despite input standardization, and offers an efficient paradigm: large context window + small fixed input + lightweight network, that maintains high accuracy while significantly reducing computational costs. This approach provides a practical pathway for deploying deep learning models in resource-limited geophysical applications.