The invention discloses a user electricity consumption prediction method based on a Prophet-LSTM model, and the method comprises the following steps: S1, obtaining the historical data of the electricity consumption of a user through an intelligent electric meter, wherein the historical data comprise time series data, weather and temperature data, and holiday and festival data; S2, preprocessing and normalizing the historical data, wherein the original power consumption data is X={x1, x2,..., xn}, and the preprocessing of the original data comprises the processing of missing values, abnormal values, repeated values and invalid values; S3, constructing a Prophet prediction model, inputting the processed historical electricity consumption data X'={x'1, x'2,..., x'n} into the Prophet model, and performing Prophet prediction; S4, in order to prevent prediction overfitting, performing combined prediction in combination with an improved long and short term memory (LSTM) network model; and S5, measuring and verifying the fitting degree and the prediction effect of the combined model, and using common evaluation indexes. According to the method, the characteristics and rules of the power consumption data are analyzed, the accuracy of the prediction model is improved, and the method has important guiding significance for making effective power supply services by the state grid and each power supply company.