Data is the key basis for the stable operation of the power grid dispatching control system, in the process of data collection, the lack of data due to hardware failure and other reasons will affect the integrity of the system data, which will have a corresponding impact on the intelligence and efficiency of power grid dispatching. Therefore, the accurate prediction of missing data is of great significance for the construction of smart grid dispatching system. In order to solve the problem of missing data prediction of electric energy collection system in the field of power grid, this study improves and optimizes the existing joint prediction method based on CNN and LSTM, adds a modified model on the basis of the joint prediction model, and uses CNN convolution neural network and the unique opposite side data scene modeling in the electric power data for different missing data segments. The experimental results show that this method reduces the average absolute error value to 0.142, which improves the accuracy of the existing prediction model and accuracy guarantee for the intelligence and efficiency of power grid dispatching system.