Abstract: In the context of digital operation and maintenance of power grids, the surge in unstructured transformer data has made defect information extraction and fault tracing challenging, hindering the transition to intelligent maintenance. Knowledge graph technology offers a potential solution by leveraging its structured nature to integrate operational data and improve efficiency. Inspired by this, this paper proposes a unified short-text format for electrical equipment defects and constructs a high-quality transformer defect relationship dataset. The ET-FSUIE (Electrical Transformer-Fuzzy Span Universal Information Extraction) model is introduced, which integrates a 20% pruned Roformer v2 pre-trained language model. By utilizing its rotary position encoding, the model effectively handles variations in defect description text lengths, enhancing text comprehension. Additionally, a W-FSL (Wasserstein-Fuzzy Span Loss) loss function based on Wasserstein distance is proposed to overcome the limitations of traditional loss functions and improve extraction accuracy. Experimental results on both public and self-built datasets demonstrate the superior performance of the ET-FSUIE model, achieving F₁ scores of 81.84% and 88.67%. Finally, a knowledge graph for power transformer defect relationships is constructed using the extracted triplets, providing robust support for the intelligent transformation of power equipment operation and maintenance.