The study of freeze-thaw (F-T) damage of steam cured concrete (SCC) plays a positive role in promoting the development of prefabricated technology in water conservancy project construction and construction industrialization in cold areas. This study aims to investigate and provide a better understanding of the surface damage pattern and pore structure of SCC. In this paper, a 3D scanner was used to scan the surface topography of SCC under five conditions of F-T (the number of F-T was 0, 50, 100, 150, and 200 respectively), and the spatial distribution of point cloud data generated by scanning results was analyzed, and surface roughness was introduced to quantify the damage effect of F-T cycle on the surface of SCC. The results show that the fluctuation range of point cloud data increases from 0-0.15 mm to 0-0.93 mm with the increase of F-T cycles. The F-T cycles result in the continuous deterioration of the surface of SCC, and surface roughness increases from 0.097 mm to 0.899 mm, and the development law basically conforms to the exponential growth law. In addition, the pore structure parameters of SCC under different F-T cycles were obtained based on X-ray computed tomography technology (XCT). It was found that the pore size and porosity increased with the increase of F-T cycles, and the growth rates ranged from 3.41% to 19.69% and 29.29% to 41.85%, respectively. The pore numbers showed a decreasing trend, with the decreasing rate ranging from 2.56% to 11.56%. It is also found that the inhomogeneity of pore space distribution caused by F-T cycles may be one of the main reasons affecting the mechanical properties of SCC. On this basis, the surface fractal dimension and volume fractal dimension of SCC pores were calculated respectively, and they were used to evaluate the F-T damage degree of SCC under different F-T cycles. The results revealed that there is a linear relationship between fractal dimension and F-T cycles. When the fractal dimension is used to characterize the F-T damage degree of SCC, the result of the volume fractal dimension is better than the surface fractal dimension.