When an electric vehicle （EV） is connected to a charging pile， it is very important to accurately predict the charging demand power of the battery pack of the EV to prevent the battery pack from being overcharged. Due to the complexity of the physical model of battery pack， it is usually difficult to build a power prediction method based on it， and its real-time performance is not high. In addition， the prediction accuracy of a single prediction model is low. Aiming at the above problems， combining charging data with machine learning， this paper proposes an EV charging demand power prediction method based on reinforcement learning （RL） and variable weight combination model. Firstly， based on the traditional grey wolf optimization （GWO） algorithm， chaos mapping and elite reverse learning strategy are combined to improve the quality of the initial population， and the dynamic weight strategy of reinforcement learning is used to update the individual position of grey wolf to optimize the parameters in the least square support vector machine （LSSVM） algorithm. Then， the weights of the extreme learning machine prediction model and the improved LSSVM prediction model are reasonably distributed by the variable weight combination method based on time-varying weight distribution， so as to solve the shortcomings of the single prediction model method. Finally， the actual charging data of electric vehicles are used to verify the proposed prediction algorithm. Compared with the other three traditional methods， the prediction accuracy of the new method is improved by 4.75%， 3.84% and 0.38%， respectively.