Abstract: To address the challenge of accurately identifying fine fractures in CT scan images of coal and rock, this study proposes a fracture segmentation model based on explicit visual prompting for coal and rock CT images (EViP-CTCrack). The model is validated on a self-constructed coal and rock CT image dataset, CTrock. EViP-CTCrack primarily consists of components such as a residual mixed connection convolution module, a cross-attention upsampling module, a multi-representative vector classifier, and an explicit visual prompting generator. Experimental results demonstrate that EViP-CTCrack achieves an average intersection over union (IoU) of 88.1% and an average precision of 94.4% on the CTrock dataset, yielding promising fracture segmentation performance. Finally, the model is applied to fracture recognition in mining boreholes, where a porosity-compressive strength equation is established, facilitating the rapid estimation of uniaxial compressive strength.