Aiming at the shortcoming of the singular optimization objective in the general research of the Wireless Sensor Network (WSN) multi-target cooperative tracking, a new multi-target coupling cooperative task allocation method based on Q learning is proposed. A cluster merging method is used to solve the competition conflict problem in task allocation in the case of multi-target coupling. Firstly, Q learning method is used to select the optimal time for merging clusters at the stage of multi-target encountering. At the same time, the suitable Q learning function and return function are found out, and the most suitable switching scheme of the cluster head for the stage of target parallel moving is obtained. Then, under the premise of ensuring sufficient remaining energy, the schemes to select the optimal cluster heads and cluster members of different stages are given by using the information utility function and the number of the smallest cluster members. Finally, the target information is separated according to the target tag. The simulation results show that the algorithm can optimize the multi-target coupling, which can satisfy the demand for tracking precision, and has the advantage of reducing the system energy consumption and prolonging the life cycle of the network.