The complex buried fracture zone in the N-region of the South China Sea presents significant challenges for fracture prediction due to its deep burial, weak seismic reflection signals, steep dips, and low-frequency characteristics. Conventional fracture prediction methods often fail to accurately delineate the fracture development zones and their distribution patterns. This study proposes a novel complex buried fracture zone prediction technique suitable for the area, focusing on fault signal enhancement processing. First, the seismic data undergoes structure-oriented filtering, followed by texture filtering using a grayscale co-occurrence matrix calculated along different directions. This yields fracture-sensitive data volumes in multiple orientations, which are then enhanced through coherence processing. The optimal fracture-sensitive data volume is selected, and multi-attribute fusion is performed by combining coherence enhancement and curvature. In application to the study area, this method outperforms single coherence enhancement and curvature results, providing a more detailed representation of fracture zones at various scales and clearly depicting fault characteristics, and improve the accuracy of fracture prediction.