The paper develops a target defense game-based model for addressing the many-to-one pursuit-evasion problem under complete information conditions. In this problem setting, a single attacker, multiple defenders, and a static target are considered by integrating game theory with model predictive control (MPC) through a trade-off parameter design. This enables the defenders to dynamically balance their strategies in terms of intercepting the attacker and protecting the target simultaneously. Compared to the conventional nonlinear MPC methods, the proposed algorithm demonstrates superior performance in attacker interception success rate, target protection effectiveness, and defensive coordination efficiency by virtue of combining game-theoretic strategy optimization, MPC-based motion planning, and adaptive parameter tuning. Then, the numerical experiments evaluating system performance under various scenarios are conducted, which validates the method's effectiveness in achieving optimal defense strategies while maintaining computational feasibility. In addition, the results show that the balanced approach between pursuit and protection objectives is also offering significant improvements over traditional methods.