A guided Minimax-DDQN （Minimax-Double Deep Q-Network） algorithm was designed to solve the problems of unpredictable enemy aircraft maneuver strategy and low winning rate， which are caused by the complex environment information and strong confrontation of Unmanned Aerial Vehicle （UAV） in air combat. Firstly， on the basis of Minimax decision-making method， a guided strategy exploration mechanism was proposed. Then， combined with the guided Minimax strategy， a type of DDQN （Double Deep Q-Network） algorithm was designed to improve the update efficiency of Q-network. Finally， an advanced three-stage network training method was proposed. And through confrontation training between different decision models， better optimized decision model was obtained. Experimental results show that compared with Minimax-DQN （Minimax-DQN）， Minimax-DDQN and other algorithms， the proposed algorithm has the success rate of chasing straight target improved by 14% to 60% and the winning rate against DDQN algorithm over 60%. It can be seen that compared with algorithms such as DDQN and Minimax-DDQN， the proposed algorithm has stronger decision-making capability and better adaptability in high confrontation combat environment.

Existing evidential reasoning methods have fixed model structure， single information processing mode and reasoning mechanism， making these methods difficult to be applied to target identification in an environment with a variety of incomplete information such as uncertain， error and missing information. To address this problem， a Switching Reasoning Evidential Network （SR-EN） method was proposed. Firstly， a multi-template network model was constructed considering evidence-node deletion and other situations. Then， conditional correlation between each evidence variable and target type was analyzed to establish an reasoning rule base for incomplete information. Finally， an intelligent spatio-temporal fusion reasoning method based on three evidence input and correction methods was proposed. Compared with traditional Evidential Network （EN） and combination methods of two information correction methods， such as EN and Technique for Order Preference by Similarity to an Ideal Solution （TOPSIS）， SR-EN can achieve continuous and accurate identification for aerial targets under multiple types of random incomplete information while ensuring reasoning timeliness. Experimental results show that SR-EN can realize adaptive switching of evidence processing methods， network structures and fusion rules among nodes in continuous reasoning process through effective identification of various types of incomplete information.