Aerial target threat assessment remains a critical component in modern military operations，particularly in highly dynamic and uncertain combat environments.The traditional methods are difficult to effectively handle multi-target threats，real-time decision-making，and environmental uncertainties.To address these limitations，this paper proposed a DCA-AEST framework which combines two novel modules：dynamic cross-attention feature extraction (DCAFE) and adaptive entropy SAC-Twin (AEST).The DCAFE module utilizes a hierarchical cross-attention mechanism to dynamically extract high-order feature interactions from complex multi-source battlefield data，thereby enhancing the accuracy of threat detection and prioritization.The AEST module integrates the reinforcement learning with the expert-guided reward shaping and adaptive entropy regularization，allowing the module to adaptively optimize its threat evaluation strategy in real-time.The proposed DCA-AEST framework is rigorously validated through extensive experiments in a high-fidelity adversarial combat simulation environment.The results demonstrate that DCA-AEST framework has superior performance in comparison to state-of-the-art models，showcasing significant improvements in adaptability，decision-making stability，and threat assessment accuracy in dynamic and uncertain combat scenarios.