Power transmission and transformation equipment is an important part of the power grid, and its state value represents the basic operation of the equipment. Due to some uncontrollable factors, there are some null values in the collection. These missing values mean not only information gaps, but more importantly, they will affect the subsequent data mining. In this paper, a method of missing value imputation based on Long Short-Term Memory networks (LSTM) is proposed. Compared with the classical data mining method, the results show that the use of the LSTM-based method has a 20% improvement on the root mean square error. At the same time, we also considers the influence of other different state variables and meteorological factors under the same equipment. Finally, the method is used to fill the missing data in State Grid. The results show that the proposed method can meet the requirement of filling under the normal conditions.