To address the issue of resource allocation for multiple electronic jamming unmanned aerial vehicles （UAVs） against multiple multifunctional radars in the low-altitude intelligent network cooperative cognitive jamming decision-making process， a cognitive jamming decision-making approach based on digital twinning and deep reinforcement learning is proposed. Firstly， a cognitive jamming decision-making system model is established by treating the cooperative electronic jamming problem as a Markov decision process. Considering the constraints related to jamming target， jamming power， and jamming pattern selection comprehensively， the agents’ action space， state space， and reward function are constructed. Secondly， an adaptive learning rate proximal policy optimization （APPO） algorithm is proposed based on the proximal policy optimization （PPO） algorithm. Additionally， to enhance the training speed of the deep reinforcement learning algorithm in a high-fidelity manner， a digital twin-based cooperative electronic jamming decision-making model training method is presented. Simulation results demonstrate that compared with existing deep reinforcement learning algorithms， the interference efficiency of the APPO algorithm is improved by more than 30%， and the proposed training method increases the model training speed by more than 50%.