Abstract: To address the engineering challenges of difficult hydraulic-fracture network morphology prediction and time-consuming numerical simulation, this study develops an FC-UNet surrogate model for two-dimensional fracture morphology prediction in horizontal wells. The model uses U-Net as the backbone and integrates fast Fourier transform (FFT), parallel convolution (PC), and a channel attention mechanism (CAM) to adaptively fuse pumping time-series signals with spatial geological features, thereby transforming fracture propagation prediction into a pixel-level segmentation task. Based on an unconventional fracture model (UFM) calibrated with field micro-seismic data, 310 forward-modeling samples were generated. A joint loss function combining weighted cross-entropy and Dice loss was introduced to alleviate class imbalance caused by sparse fracture pixels. Independent test results show that FC-UNet can accurately characterize main-fracture penetration and fracture deflection features, achieving an intersection over union (IoU) of approximately 0.84 and an F1-score consistently above 0.91. The prediction time for a single case is about 5 s, indicating that the model can serve as a fast surrogate for UFM-based fracture morphology forward modeling under similar geological conditions.