Cybersecurity situational awareness technology plays a critical role in assessing network security status, predicting potential attack paths, and assisting administrators in implementing effective defenses. Traditional methods for network situation assessment mostly rely on theoretical analysis, limiting their practicality in real-world networks. Additionally, the complexity of sensor-collected data often results in excessive storage demands. To address these challenges, this paper proposes a dynamic network attack-defense perception model that integrates reinforcement learning and game theory to enhance situational awareness and predict potential attack paths. The approach begins with the design of a hierarchical analytic process using a priority relation matrix to calculate system losses and assess security posture. Next, the Boltzmann probability distribution is employed to calculate the mixed-strategy Nash equilibrium, identifying optimal strategic responses. Finally, an improved Q-learning algorithm, in combination with game-theoretic principles, is used to dynamically model network state transitions, enabling accurate prediction of attack paths and supporting defenders in selecting optimal defense strategies. Simulation results validate the model’s effectiveness and practicality in complex network environments.