Accurate evaluation of rock mechanical properties in shale reservoirs is essential for optimizing hydraulic fracturing design and achieving efficient development. To address the challenges in conventional petrophysical evaluation caused by complex mineral composition and strong heterogeneity of shale in the Qingshankou Formation, Songliao Basin, a novel well logging evaluation framework based on ensemble learning algorithms is proposed. This framework utilizes conventional well logging data to q