As an embodiment of the core 6G vision of scenario-based mobile communication， the 5G kite private network is designed to address practical challenges in the power industry such as high-frequency terminal mobility， dynamic topological reconfiguration， and low-latency service guarantees. To tackle the issues of insufficient accuracy and weak adaptability in security situation awareness within highly mobile and elastic topological environments of 5G kite private networks， this paper proposes a spatiotemporal dual-modal based elastic security situation awareness method for power networks. The approach constructs a multi-source input vector by collecting core feature data， employs long short-term memory （LSTM） networks for temporal anomaly detection， and integrates isolation forest for spatial anomaly detection， thereby generating an enhanced feature matrix. Furthermore， a bisecting K-means clustering algorithm and a dynamic weight adjustment mechanism are introduced to adaptively optimize feature weights， enabling accurate classification of security situations into three levels： Stable， vulnerable， and threatened. Finally， the weighted principal component analysis is applied for dimensionality reduction and visualization. Simulation results demonstrate that the proposed method effectively responds to changes in network state， improves situational judgment accuracy across various scenarios， and provides reliable support for the security maintenance of 5G kite private networks.