The invention discloses a power distribution transformer area electricity sales accurate prediction method based on a modal GRU learning network, which comprises the following steps of: S1, obtaininghistorical data of electricity sales of a power distribution transformer area, and dividing the historical data into a test set and a training set; S2, preprocessing the data, complementing the sampling time points to ensure continuity of the sampling time points, and filling up missing data of the sampling points by utilizing an average interpolation method; S3, determining an optimal modal number K of variational mode decomposition (VMD) according to the center frequency of each modal component by using an experimental method; S4, carrying out VMD decomposition on the historical data of theelectricity sales of the transformer area, and respectively extracting a decomposed low-frequency modal component and a decomposed high-frequency modal component; S5, predicting a low-frequency mode and a high-frequency mode respectively by using a Prophet prediction model and a GRU learning network; and S6, reconstructing the prediction result of each mode, and obtaining a predicted value of theelectricity sales of the transformer area. The method can improve the prediction precision of the electricity sales of the transformer area, and can provide theoretical and practical support for the precise prediction and management of the electricity sales of the transformer area.