Aiming at the problem of long time and low success rate of multi-robot cooperative rounding in unknown dynamic environment, a new multi-robot cooperative rounding method based on biologically inspired neural network is proposed. First, a multi-robot collaborative rounding model is built, and the dynamic alliance strategy is used to realize the linkage of multiple robots. Second, a tracking strategy based on biologically inspired neural networks is constructed to dynamically guide all robots in the alliance to track. Finally, a formation strategy is used to achieve the target rounding. The experimental results show that the average capture time of the proposed method is 12.7s, 22.3s, 34.2s, 17.7s and 28.5s under the conditions of single target, multiple targets, partial robot failures, obstacles of different shapes, and different regular environments. The average capture success rate is 97.4%; compared with other multi-robot cooperative hunting algorithms, the algorithm proposed in this paper has advantages in capture time and capture success rate.