在复杂电磁环境下，战术无线通信链路面临严重的干扰威胁，易导致通信中断，影响任务执行的稳定性与可靠性。为提升无线通信系统在动态干扰场景中的抗干扰能力，本文提出一种融合智能超表面（RIS）与深度强化学习的自适应抗干扰架构。该架构从增强有效信号强度和生成动态抗干扰策略两个维度出发，协同提升通信链路的鲁棒性和智能决策能力。在方法设计上，首先利用RIS的波束赋形能力，实现对无线传播环境的主动调控，从而提升用户信噪比，有效抑制干扰并加速学习策略的收敛；其次将频点选择与功率控制建模为马尔可夫决策过程，引入结合历史价值评估的贪婪动作选择策略，构建基于双深度Q网络（Double DQN）和优先经验回放机制的强化学习框架，通过RIS增强的信号提升策略稳定性，并显著缩短训练周期。仿真结果显示，在宽带扫频、随机脉冲及智能博弈等典型干扰场景中，所提方法的平均通信成功率相比仅采用深度强化学习或RIS的单一方案提升超过15%，充分验证了所提架构在高动态环境下的鲁棒性与广泛适应能力。

In complex electromagnetic environments, tactical wireless communication links face severe jamming threats, which can easily lead to communication disruption and adversely affect the stability and reliability of mission execution. To enhance the anti-jamming capability of wireless communication systems in dynamic interference scenarios, this paper proposes an adaptive anti-jamming architecture that integrates Reconfigurable Intelligent Surfaces (RIS) with Deep Reinforcement Learning (DRL). The proposed architecture improves the robustness of the communication link and the intelligence of decision-making from two dimensions: enhancing the strength of useful signals and generating dynamic anti-jamming strategies. In terms of methodology, the system first leverages the beamforming capability of RIS to actively manipulate the wireless propagation environment, thereby improving the channel signal-to-noise ratio, effectively suppressing interference, and accelerating the convergence of learning strategies. Next, frequency selection and power control are modeled as a Markov Decision Process. A greedy action selection strategy incorporating historical value estimation is introduced, forming a reinforcement learning framework based on Double Deep Q-Networks (Double DQN) with prioritized experience replay. The RIS-enhanced signal improves the stability of the learning strategy and significantly shortens the training period. Simulation results demonstrate that under typical jamming scenarios—such as wideband frequency sweeping, random pulse jamming, and intelligent adversarial games—the proposed method achieves an average communication success rate improvement of over 15% compared to approaches that employ either DRL or RIS alone. These results validate the robustness and broad adaptability of the proposed architecture in highly dynamic environments.