Abstract: The uncertainty and long-term prediction of the load fluctuation of electric vehicle（EV） charging stations pose significant challenges to accurately predict the charging load. An EV charging load prediction based on dynamic adaptive graph neural network is proposed. Firstly, a spatiotemporal correlation feature extraction layer for charging load information is constructed. By combining multi-head attention mechanism with adaptive relevance graph, a comprehensive feature representation with spatiotemporal correlation is generated to capture the load fluctuation of EV charging station. Then, the extracted features are input into a spatiotemporal convolutional layer to capture the coupling relationship between time and space. The ability of the model to couple long time series is enhanced by Chebyshev polynomial graph convolution and multi-scale temporal convolution. The effectiveness of the algorithm has been verified using two real datasets. Taking the Palo Alto dataset as an example, compared with existing methods, the average prediction error of this algorithm under 4 volatile conditions is reduced sharply.