To address the challenge of difficult identification of concrete cracks in extremely dark environments, a concrete crack recognition method based on biomimetic tactile technology was proposed. By simulating the tactile perception mechanism of organisms, a tactile sensing system and a deep learning model were constructed to achieve crack detection in dark environments. A high-sensitivity silicone tactile probe was designed and combined with a multi-light source imaging system and the Lambertian reflection model, and the three-dimensional texture of cracks was reconstructed through elastomer deformation and light-shadow gradient changes, overcoming the reliance on visual information in dark environments. On this basis, a tactile segmentation network for concrete cracks (TSCC-Net) was constructed. Through the collaboration of a semantic perception extractor, mutual fusion module, and auxiliary supervision module, precise segmentation of cracks in tactile images was achieved. Experimental results showed that TSCC-Net achieved an IOU of 86.3% in tactile image crack recognition, with a model parameter size of 12.86 MB and an inference speed of 114.5 fps, significantly outperforming traditional visual models. In particular, TSCC-Net demonstrated stronger robustness in narrow cracks and areas with complex textures compared to models such as U-Net, Segformer, and DeepLabV3+.