  In this paper, we propose a method for knowledge graph construction in power
distribution networks. This method leverages entity features, which involve
their semantic, phonetic, and syntactic characteristics, in both the knowledge
graph of distribution network and the dispatching texts. An enhanced model
based on Convolutional Neural Network, is utilized for effectively matching
dispatch text entities with those in the knowledge graph. The effectiveness of
this model is evaluated through experiments in real-world power distribution
dispatch scenarios. The results indicate that, compared with the baselines, the
proposed model excels in linking a variety of entity types, demonstrating high
overall accuracy in power distribution knowledge graph construction task.
