Abstract: The current method of using intelligent algorithms, which instead of greedy strategy to solve the influence of seed sets, has greatly shortened the running time. However, some intelligent algorithms have unstable search capabilities and fail to achieve the expected results due to difficulty in parameter set, and most of the experiments are only carried out on small networks, not medium and large networks. Aiming at this problem, an adaptive chaotic genetic algorithm is proposed. Firstly, the first-order tolerance activation set is introduced into the genetic algorithm as a fitness function, which the purpose is to evaluate the expected influence of the seed set in the iterative process. Then, the Logistic chaotic sequence is used to optimize gene selection of crossover and mutation. Finally, the self-adaptive mutation mechanism search for the optimal seed set. The experiments on four real networks show that the algorithm runs efficiently and the seed set has a wider range of influence.