低轨卫星网络的时变特性以及地面站分布不均衡等问题，对网络的鲁棒性优化及流量的高效管理提出了严峻挑战。为此，本文提出了一种融合多维时空特征的低轨卫星关键节点评估算法，旨在精准识别维持地面站间高效通信的关键节点。该算法基于星间与星地网络的双层交互关系构造时变拓扑图，依据局部结构属性和全局依赖关系设计了多维节点特征体系，融合多层图卷积网络与长短期记忆网络构建多维时空特征提取模型，捕捉网络时空演化规律并完成节点重要度评价。仿真结果表明本文算法在评估结果准确度与时序稳定性方面均具有明显优势，对基于该算法识别的关键节点实施分流策略能够有效缓解高负载下的网络拥塞情况，为卫星网络的负载优化策略提供了新的研究思路。

The time-varying characteristics of Low Earth Orbit (LEO) satellite networks and the unbalanced distribution of ground stations pose severe challenges to network robustness optimization and efficient traffic management. To address these issues, this paper proposes a LEO satellite key node evaluation algorithm fusing multi-dimensional spatiotemporal features, aiming to accurately identify the key nodes that maintain efficient communication between ground stations. The algorithm constructs a time-varying topological graph based on the two-layer interaction between inter-satellite and satellite-ground networks, designs a multi-dimensional node feature system from the perspectives of local structural attributes and global dependency relationships, and establishes a multi-dimensional spatiotemporal feature extraction model by integrating Multi-Layer Graph Convolutional Networks (MLGCNs) and Long Short-Term Memory (LSTM) networks. This model captures the spatiotemporal evolution law of the network and completes the node importance evaluation. Simulation results show that the proposed algorithm has significant advantages in both the accuracy of evaluation results and temporal stability; implementing a traffic diversion strategy based on the key nodes identified by the algorithm can effectively alleviate network congestion in high-load scenarios, providing a new research idea for the load optimization strategy of satellite networks.