CdZnTe crystal material is the preferred substrate material for third-generation high-performance, long/very long wave cadmium telluride infrared focal plane detectors. However, due to the inherent characteristics of CdZnTe material, the presence of polycrystalline and twin regions in the grown crystals affects device performance. At present, the main method for cutting single crystal wafers is through manual identification of single crystal regions, resulting in low efficiency and unclear contour recognition. In this paper, based on a multi-angle wafer surface topography visual recognition device, neural network-based image segmentation technology has been applied to the identification of single-crystal regions in Te-Zn-Cd wafers, enabling the automatic differentiation between Te-Zn-Cd single-crystal and polycrystalline regions. This provides a foundation for the automatic cutting process of unit price regions.