The efficiency of intelligent decision-making in traditional multi-aircraft air combat is low, making it difficult to meet the demands of complex air combat environments and resulting in unreasonable target allocation. This paper presents a reinforcement learning-based method for intelligent decision-making and target assignment in multi-aircraft air combat. Long and short term memory networks are utilized for feature extraction and situation awareness of the state. The residual network and value network are trained using normalized state information after feature fusion. The agent selects the optimal action based on the current situation through near-end optimization strategy. A comprehensive threat degree is calculated based on the threat assessment index, prioritizing attack targets with higher threat values among warplanes. To validate the algorithm's effectiveness, a 4v4 multi-aircraft air combat experiment was conducted in a digital twin simulation environment developed by our research group. The results were compared with mainstream reinforcement learning algorithms within the same experimental environment, demonstrating that our proposed algorithm achieved significantly better victory rates in multi-aircraft air combat than other mainstream reinforcement learning algorithms, thus confirming its efficacy.