A chaotic coyote optimization algorithm based on inverse time-decay operators, named ICCOA, is proposed to solve the problems of the coyote optimization algorithm(COA), such as the poor performance and low diversity. Firstly, the inverse time decay weight factor is added in the process of individual iterative updating, so as to maintain the balance between global search and local development ability and improve the search speed of the algorithm. Secondly, the chaotic interference mechanism based on the Tent chaotic map is added, and some poor individuals in the population are mapped to produce new individuals, thus increasing the diversity of the population. In order to verify the optimization ability of the ICCOA, functional optimization tests are carried out in 10, 30 and 100 dimensions respectively, and compared with five optimization algorithms. The experimental results show that the ICCOA has good optimization performance. Finally, the ICCOA is applied to the parameter optimization of the BP neural network, and a new neural network model BP neural network with ICCOA(ICCOABP) is proposed. Compared with the standard neural network and the BP neural network parameter optimization method based on the genetic algorithm, the experimental results show the efficiency of the ICCOABP algorithm.