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Abstract: Natural fractures serve as the primary storage spaces and flow pathways in deep to ultra-deep tight sandstone reservoirs, directly influencing hydrocarbon accumulation, preservation, and production. Borehole images offer intuitive, continuous, and high-resolution identification of natural fractures along the entire borehole. However, relying solely on complete sinusoidal curves from borehole images for fracture identification may lead to omissions, as it overlooks cases where these curves are incomplete or truncated. To address the problems and deficiencies in fracture identification, this study systematically classifies borehole image feature patterns based on core-to-log spatial position restoring. A bidirectional comparison is conducted between natural fractures in cores and the fracture image features in borehole images. A quantitative relationship between fracture dip angle, thin layer thickness and borehole radius was established, accompanied by a mathematical expression describing the fracture curve morphology was proposed. These findings enabled the development of an imaging response pattern for natural fractures in deep and ultra-deep tight sandstone reservoirs, incorporating key parameters such as dip angle, through-layer connectivity, and spatial position within the borehole. In the Bashijiqike–Baxigai tight-sandstone reservoirs of the Bozi–Dabei area, we estimate that approximately 24% of core-observed fractures display distinct linear-pattern features on borehole images, whereas approximately 91% of borehole images features can be correlated with fractures observed in core. Fracture identification rates for natural fractures increased by 17% in water-based mud and by 3% in oil-based mud through the application of the natural fracture image response pattern. Moreover, this study analyzes the deviations in the matching between core fractures and image features. Finally, we further discuss the common sources of error in natural fracture identification using borehole images from multiple perspectives, including missing core responses, inconsistencies between core and borehole image features, distortion of fracture chord curve, inaccurate fracture count, misclassification of fractures, and variations in interpretation under different mud systems. The research addresses the blind spots of traditional methods in fracture identification within thin layers, not only enhancing the detection rate of natural fractures but also further improving the accuracy of fracture recognition. At the same time, it will contribute to the optimization of fracture characterization, reservoir evaluation, and production forecasting, providing a more reliable data foundation for exploration and development under complex geological conditions.