Reasonable selection of combat resources to form a sensor-weapon-target kill-chains plays an important role in air defense network operations. This paper studies sensor-weapon-target assignment （S-W-TA） under multiple constraints and multiple optimization indexes， and proposes an deep reinforcement learning based on allocation method. The mathematical model of S-W-TA problem is established， and the concept of kill chain advantage is used to integrate the traditional efficiency index. The deep Q network （DQN） training agent is used to solve the S-W-TA problem by deep reinforcement learning method for the first time. The simulation results show that the solution obtained by the deep reinforcement learning algorithm is superior to the rule-based allocation method widely used in engineering， and the reinforcement learning algorithm is more suitable for solving the S-W-TA problem with multiple constraints and multiple optimization indexes， and has certain engineering application value.