The limitations of conventional analysis techniques have emphasized the need for intuitive and informative visualization methods that effectively capture system dynamics and interdependencies, thereby supporting transient stability analysis and system monitoring. Traditional visualization approaches often lack the ability to represent the topological structure of the network and dynamic variations of critical node features such as voltage magnitude, rotor angle, and angular velocity. To address these limitations, this paper proposes a transient feature image representation method that integrates Laplacian force-directed layout with artificial immune optimization. The method constructs a topology-aware initial layout based on the Laplacian matrix of the power network and employs an immune-inspired algorithm to optimize node positions according to transient feature divergence. This enables the generation of clear, feature-sensitive images that reflect both structural relationships and dynamic system behavior. The proposed approach is validated on the IEEE 39-bus system, demonstrating its effectiveness in revealing transient characteristics and supporting visual analysis of system responses under disturbance scenarios.