The ever-changing battlefield environment requires the use of robust and adaptive technologies integrated into a reliable platform. Unmanned combat aerial vehicles (UCAVs) aim to inte-grate such advanced technologies while increasing the tactical ca-pabilities of combat aircraft. As a research object, common UCAV uses the neural network fitting strategy to obtain values of attack areas. However, this simple strategy cannot cope with complex en-vironmental changes and autonomously optimize decision-making problems. To solve the problem, this paper proposes a new deep deterministic policy gradient (DDPG) strategy based on deep re-inforcement leing for the attack area fitting of UCAVs in the future battlefield. Simulation results show that the autonomy and environmental adaptability of UCAVs in the future battlefield wil be improved based on the new DDPG algorithm and the training pro-cess converges quickly. We can obtain the optimal values of attack areas in real time during the whole flight with the wel-trained deep network.