Precipitation nowcasting plays an important supporting role in forecasting severe convective weather. In meteorological services, the radar echo extrapolation method is mainly used to solve precipitation nowcasting problems. However, existing methods often lack the ability to effectively learn from sequential radar data, resulting in poor prediction accuracy. In order to solve this problem, this paper proposed ASTGCN(A Spatio-Temporal Graph Convolution Neural Network) for nowcasting of severe convective precipitation. Efficiently capture the temporal dependence between adjacent radar frames using a spatio-temporal graph convolutional network. In addition, an attention mechanism and an autoencoder were utilized to enhance the model's ability to capture spatio-temporal correlations. Experimental results show that the model can discover hidden graph structures from data and thereby capture hidden spatial relationships. Compared with the existing model (Transformer), the Critical Success Index (CSI) of this model is improved by 28%, indicating its superior performance in the nowcasting of severe convective precipitation.