In view of the increasingly complex topology structure of power grid, it is difficult to quickly mine effective fault information from massive data after a fault and has high computational complexity, and the fault data is incomplete and uncertain, leading to the failure to get correct diagnosis results. To solve this problem, this paper introduces the self-coding association rule mining algorithm (ACT-Apriori) into power grid fault diagnosis. The initial fault decision table was established by taking the protection and circuit breaker action data as the condition attribute and the fault line as the decision attribute. Then a self-coding association rule mining algorithm is used to extract kernel attributes and the optimal threshold is determined by dynamic threshold interactive mining technology. Finally, the simplest fault decision table is formed, and the fault information of each case is diagnosed and reasoned. In this paper, the four-bus distribution system is used as the simulation object, and compared with the traditional Apriori algorithm, FP-growth algorithm and the latest FP-Network algorithm, the calculation results show that: Compared with the traditional association rule algorithm, the running time of the improved algorithm is reduced by 90.69% and 83.55%, and the memory footprint is reduced by 21.43% and 15.38%, respectively. Compared with the FP-Network algorithm, the time complexity and space complexity are optimized to a certain extent. In addition, the proposed algorithm has high fault tolerance for single, double and rare faults with incomplete fault data, and the accuracy is 95.24%, which can effectively achieve rapid fault diagnosis.