Abstract: With the continuous development of power grids, power cables are widely used in urban power grids, but cables are generally laid underground, not easy to find fault and long recovery time, the reliability of power supply was seriously threatened, Traditional planned maintenance has not been able to solve the existing problems accurately. Therefore, it was of great significance to improve the reliability of power distribution and reduce the failure rate by assessing the operational status of power cables through the relevance analysis of massive monitoring and testing data. This paper proposes a power cable state assessment algorithm based on association rule data mining technology from the perspective of big data and information relevance. Firstly, by analyzing the online monitoring data, detection data, experimental data, and the interrelation of the state parameters of the cable and transformer fault types, the set of cable synthetic status parameters can be set up. Then, use the association rule method to calculate the weight coefficient of each state parameters; In order to effectively avoid the influence of subjective factors on the evaluation results, weighted theory of equilibrium function was used to calculate the weight coefficient of the synthetic status parameters; Finally, combined with the existing assessment process, an accurate and objective evaluation system power cable was established.The results of the example verify the validity and accuracy of the assessment method in this paper, which provides a new novel idea for cable status assessment.