The application of deep learning methods in the research on intelligent recognition of fault images in Electric Multiple Units (EMUs) represents a crucial approach to alleviating the heavy workload of Trouble of moving EMU Detection System (TEDS) inspections and safeguarding train safety. In light of the current TEDS image data’s problems such as irregular formats, substantial quality disparities, and uneven sample distribution, which pose challenges for deep learning, we proposes a method for constructing an intelligent recognition dataset of TEDS EMU fault images. By integrating the characteristics of TEDS images with the prior knowledge of EMU operations, a unified data acquisition protocol and standardized dataset construction techniques are devised. Consequently, an intelligent recognition dataset of TEDS EMU fault images is established. This dataset accomplishes effective data integration and standardization and has been successfully implemented in the research on automatic Recognition Algorithms for TEDS EMU faults. It accelerates the rapid progress of intelligent recognition technology for TEDS fault images and lays a robust technical foundation for ensuring the safety of EMU operations.