In order to reduce the average correct deviation of the online condition monitoring method of distribution network equipment and improve the monitoring recall rate, the operation condition monitoring method of distribution network equipment integrating digital twin technology and association rules is studied. Innovatively utilizing digital twin technology to construct a data interaction model between physical entities and virtual models of distribution network equipment; Based on association rules, extract key state parameters of device operation, and further explore the correlation between device items and attributes in the digital twin model. The K-means algorithm is used to discretize the amplitude of the running data, and the initial threshold is obtained through residual calculation model. By comparing the relationship between the prediction error of offline models and the preset initial threshold, state monitoring is completed. The experimental results show that the proposed method has an average correct monitoring deviation of 1–1.2 and a monitoring recall rate of over 96%, which improves the accuracy of online monitoring of distribution network equipment and helps maintain the continuous, effective, safe and stable operation of the power system.