This paper proposes an approach of jamming resource allocation based on an enhanced proximal policy optimization(PPO) algorithm to handle the jamming resource allocation problem of UAV swarms in the scenario of high-dynamic communication countermeasure. The enhanced PPO algorithm combines state normalization, advantage normalization, and entropy regularization mechanisms with the PPO algorithm, which is referred to as the SANER-PPO algorithm in this paper. Firstly, we aim at maximizing the number of target radios which are jammed by a UAV swarm successfully, while minimizing the sum of jamming power consumption of the UAV swarm. Then, the UAV swarm is modeled as agents, and a Markov decision process is established based on the jamming resource allocation model. Finally, an SANER-PPO algorithm is proposed to obtain optimal decisions of jamming beamforming and power allocation. When compared to the original PPO algorithm, the SANER-PPO algorithm not only incorporates a state normalization mechanism into the decision stage of the agent to improve its effectiveness, but also introduces advantage normalization and entropy regularization mechanisms to the update stage to improve the convergence speed and stability of the algorithm. Numerical results demonstrate that the performance of the proposed algorithm outperforms the original PPO algorithm and the soft actor-critic algorithm in terms of successful interference rate and jamming power consumption. In addition, ablation experiments are conducted by gradually removing the three proposed mechanisms in the algorithm, which validate the effectiveness of these mechanisms.