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In complex environments with sparse features in continuous domains, it is easy to lose drone target localization, resulting in the phenomenon of multiple drone target collaborative search paths overlapping, leading to an increase in conflict risk coefficient. Therefore, a multi drone target collaborative search control method based on dynamic Bayesian networks has been studied. Install high-resolution image sensor devices on drones to obtain target images, perform motion blur compensation on the target images using the Lucy Richardson algorithm, extract target shape and movement features using dynamic Bayesian networks, and use feature matching methods for multi drone target localization. Construct a grid map based on the image information of the target location and environment, allocate search targets in the grid map by calculating search priority, and use the allocated targets as key path points in the search route to carry out multi drone target collaborative search control path planning. The controller continuously adjusts the flight trajectory to achieve collaborative search control of multiple drone targets. The experimental test results show that the target omission rate of the design method is relatively low, the collision conflict risk coefficient is low, and the success rate of multi drone target collaborative search control is high. This proves that the method has significant advantages in multi drone target collaborative search control.