Conventional digital holography (DH) technique largely limited by the effect of random scattering media in the imaging path, which causes great challenges for its applications in vivo imaging. As an improvement, short-coherence digital holography (SCDH) uses a low-coherence light source (near-infrared (NIR) region), where the absorption of light is at a minimum, to enhance its ability to resist scattering. However, SCDH also fails under strong scattering conditions. Here we propose to use deep learning (DL) for SCDH, and the results show that an image of a target behind a 2.30 mm chicken breast tissue can be reconstructed successfully. We experimentally demonstrate that DL-based SCDH can be used to reconstruct the object from a single measurement under some hard conditions, for example, when there is strong static or dynamic scattering media in the imaging path.