Madden-Julian Oscillation (MJO) is an atmospheric oscillation phenomenon that has a huge impact on global weather. To obtain more accurate and effective MJO prediction results, we propose a temporal forecasting deep learning model that combines dynamic graph neural networks with transformers. The model is designed to identify anomalous nodes during different time periods, and feed the results into a graph neural network for encoding, while a transformer model is employed for decoding, resulting in predictive outcomes. Experimental results demonstrate that our model achieves a maximum COR effective prediction value of 39 days and an RMSE effective prediction value of 31 days, outperforming existing models and surpassing the results of traditional partial differential numerical forecasting models to some extent.