<p>Accurately capturing internal crack evolution and deformation is essential for understanding rock failure and improving disaster prediction. However, traditional methods mainly focus on surface cracks and struggle to reveal the spatial characteristics of crack propagation, limiting insights into rock damage mechanisms. This study proposes a framework for automatically and visually characterizing crack behavior and deformation evolution by integrating in-situ X-ray computed tomography (XCT), deep learning, and digital volume correlation (DVC) techniques to address this limitation. This framework enhances crack identification accuracy and internal deformation field analysis. First, in-situ XCT scanning experiments were conducted to dynamically capture the crack propagation process in sandstone samples with an initial single defect. Next, a U-Net-based image segmentation approach was employed to improve crack extraction accuracy from CT images, enabling precise characterization of internal crack behavior. Finally, DVC technology was utilized to compute three-dimensional (3D) full-field displacement and strain distributions, facilitating the visualization and quantitative analysis of internal rock deformation mechanisms. The results indicate that the displacement difference between monitoring points on both sides of a crack enables the quantitative classification of crack types. Crack behavior in sandstone generally exhibits a mixed combination of opening, shearing, and tearing modes. Moreover, the heterogeneous displacement field distribution within the sandstone influences spatial variations in crack behavior. After crack formation, the internal strain field exhibits significant localization, which largely governs the sample’s failure process. The distribution characteristics of the <i>ε</i>_<i>yy</i> and <i>ε</i>_<i>xy</i> strain fields closely correspond to the internal crack morphology, demonstrating that their evolution effectively characterizes the internal rock damage process.</p>