## 1 Introduction
In the early stage, some studies [5] achieved network traffic prediction by optimizing prediction models using simulation results through changing the learning rate of the network and the number of hidden-layer units. However, manually-designed networks are overly dependent on high-quality and large-volume data. Some studies [6] used the LightBGM multi-classification algorithm to classify network traffic and then conducted data processing and prediction research on the classified self-similar traffic with strong burstiness. By studying the influence of different parameter values on the prediction accuracy, the optimal traffic prediction model was obtained. With the development of deep learning, one study [7] proposed a method based on spatio-temporal topological graphs. This method considered the correlation between missing data and the available spatio-temporal data as well as the correlation between road network topology and traffic flow, achieving real-time and effective network traffic prediction. Nevertheless, this method is sensitive to data deviation. When considering the magnitude of traffic data, model training is prone to over-fitting. Given the changing dynamic spatio-temporal relationships among satellite nodes, more complex models should be introduced, taking into account the topological relationships and both past and future traffic conditions to achieve accurate network traffic prediction. The network structure of the proposed model is more complex, with a strong capability to learn from large-scale data. By introducing more sophisticated model architectures and regularization techniques, the model significantly reduces the sensitivity to data bias, avoiding prediction errors caused by data bias in traditional methods. The model integrates topological relationshipships and historical traffic data, employing multi-level attention mechanisms and dynamic weight adjustments, thus effectively preventing overfitting issues during training due to data scale or complexity. In recent years, the recurrent neural network (RNN) model has been widely used in time-series prediction [8]. Its architecture allows for information to circulate in the network, effectively capturing the temporal dependencies in sequential data. However, when attempting to train deep or long-time-span RNNs, phenomena such as gradient vanishing or explosion may occur, making it difficult to learn the influence of events from the distant past. Moreover, even with careful design and parameter tuning, the standard form of RNNs may still be insufficient to fully represent the correlations between historical patterns over long periods. This is particularly challenging for satellite network traffic that requires the consideration of periodicity. In the face of these challenges, the long short-term memory (LSTM) model emerged. LSTM solves the long-term dependency problem of RNNs by introducing a gating mechanism [9]. Due to its excellent ability to process sequential data, good stability, convergence, and strong robustness, it can effectively process and analyze network traffic data, achieving the accurate prediction of short- and long-term time-series problems. However, LSTM has been criticized by the academic and industrial circles because of its high computational complexity and the risk of over-fitting when the data volume is insufficient. The emergence of the gated recurrent unit (GRU) model [10] has, to some extent, perfectly solved some of the problems of LSTM. GRUsimplifies the structure of LSTM and reduces the computational complexity by merging the forget gate and the input gate into an update gate and integrating the cell state and the hidden state. In many sequential data processing tasks, GRU can achieve performances comparable to that of LSTM. Especially in tasks with strong short-term dependencies, GRU may even perform better.