Abstract: China possesses abundant shale oil resources, but exploration and development have progressed slowly due to the strong heterogeneity and complex fracture development in continental shale reservoirs. In this study, the shale oil reservoir in the second member of the Funing Formation in the well QY4 area of the Qintong Sag, Subei Basin, was taken as the research object. Through fracture identification technologies, a high-precision three-dimensional discrete fracture network (DFN) model was established to support sweet spot prediction optimization and efficient reservoir development. Through core observation, thin-section analysis, and scanning electron microscopy (SEM), fracture types, occurrence, and distribution patterns were systematically characterized. Based on conventional logging data, a novel fracture identification parameter (nfra) was constructed by comparing the response characteristics of fractured and non-fractured intervals. Fractal theory was introduced to optimize the logging curves, thereby enhancing identification sensitivity and accuracy. Combined with electrical imaging logging data, the fracture intensity of individual wells was quantitatively calculated. Under the constraints of geological structure and fracture intensity, a three-dimensional fracture intensity model and a DFN model were established to ensure consistency with actual geological conditions. The results indicated that the study area mainly developed NE-trending and NW-trending high-angle shear fractures, with NE orientation being the dominant trend, reflecting the influence of multi-stage tectonic stress fields. The improved nfra parameter after fractal processing significantly enhanced fracture identification capability, successfully identifying eight fracture-developed intervals in the second member of the Funing Formation in well QY4, with high consistency with core and imaging logging data. Fracture intensity analysis showed that the NE-trending fractures had an intensity of 0-0.5 m, and the NW-trending ranged 0~0.18 m, demonstrating distinct anisotropic characteristics. The established 3D models accurately characterize the spatial distribution of fractures, align with geological understanding, and provide a reliable basis for fracturing design and well location deployment. This integrated approach effectively addresses the problems of low accuracy in fracture identification and difficulties in modeling for continental shale oil reservoirs, thereby improving exploration and development efficiency and providing a reference for shale oil resource evaluation and development in similar basins.