The sampling-loop abnormality of smart substations has the characteristics of concealment, instantaneousness, and instability, which is difficult to be found. In this regard, a smart substations sampling-loop abnormality warning scheme based on homology recording data is proposed. First, whether the sampling loop is abnormal is judged by reasonably setting the early warning thresholds and waveform-analysis criteria. Second, based on the information of the equipment operation and maintenance manual related to the sampling loop, the knowledge map of the operation and maintenance of the sampling loop is constructed to realize the abnormal decision-making. Finally, a case study is carried out by taking the actual defect of a substation as an example. The results show that the scheme is effective in finding the abnormality of the sampling loops, and the constructed knowledge graph can be used as a reference for O&M personnel to dispose faults, which greatly alleviates the manual pressure.