This study aims to address defense strategy selection in network attack-defense confrontations and adapt to incomplete rationality scenarios in network attack-defense better. Incorporating reward-punishment mechanisms and prospect theory， the study utilized an evolutionary game model to depict the network attack-defense game process. By solving the dynamic evolution equations， it investigated the evolutionary patterns of behavioral strategy selection among the attacker， defender， and regulator. It conducted a stability analysis of the decision-making behaviors of each game participant and explored the effect of defense costs， reward-punishment mechanisms， and value functions on the selection of behavioral strategies of each party. The results show that initial decision-making probabilities do not affect the overall evolutionary trend of the game system， and the evolutionary equilibrium points possess predictability and stability. Increasing defense investment can reduce the intensity and frequency of attacks， but increased costs may curb defense investment. Implementing reward-punishment mechanisms by the regulator can deter network attacks， but it may affect the defender’s enthusiasm for investing in defense strategies. After the introduction of a value function， the decision-making of both attackers and defenders is influenced by factors such as value perception and risk preference. This can expand the gap between perceived gains and losses and actual values， thereby changing the evolutionary path of the game system.