Fractures are the main seepage channels for oil and gas migration in ultra-deep tight reservoirs, and are crucial for ultra-deep oil and gas exploration and development. Ultra-deep tight reservoirs have highly complex petrophysical characteristics under high-temperature, high-pressure environments, resulting in ambiguous and multi-solution well log responses pertaining to fractures. To solve this problem, we proposes a deep kernel method (DKM) for fracture identification in ultra-deep tight sandstones. This method employs kernel principal component analysis to extract non-linear log features associated with fractures. It utilizes a deep learning cascade structure to extensively explore the log response characteristics across various scales for accurate fracture identification. Furthermore, it employs gradient-free optimization algorithms to automatically determine the optimal model structure and parameters. We conducted a case study in the ultra-deep tight sandstone reservoirs of the Lower Cretaceous Bashijiqike Formation in the Keshen gas field, Tarim Basin, and the proposed method was applied and verified. Through sensitivity analysis of logging responses to fractures, six specific logging curves were chosen for fracture identification. The first three variables, DEN, RD, and RM, correspond to direct measurements from well logging, whereas the latter three, RSD, nT1, and nT2, are reconstructed curves specifically developed to enhance the detection of fracture-related information. This distinction effectively clarifies the differences in logging parameters between fractured and non-fractured zones. A comparative analysis between the fracture identification results and the core fracture descriptions demonstrated the accuracy of the deep kernel method in identifying fractures within ultra-deep tight sandstone formations. This method achieved an accuracy improvement of over 5% compared to the conventional multi-kernel support vector machine method, thus exhibiting robust applicability for single-well fracture identification.