Abstract: When the other parameters of active well coincidence counter (AWCC) were determined, artificial intelligence algorithm was used to find the best way to arrange the ³He tubes and improve the efficiency of neutron detection. Firstly, the whole space of the layout scheme was uniformly sampled, and Monte Carlo method was used to simulate the detection process of AWCC to calculate the neutron detection efficiency of each scheme, and generate sample data for artificial intelligence algorithm. Then the relationship between ³He tube layout scheme and neutron detection efficiency was quickly fitted by deep neural network (DNN). Finally, the optimal layout of ³He tube in AWCC was searched by genetic algorithm. The error between the neutron detection efficiency of this method and Monte Carlo calculation result is acceptable, and the optimal efficiency is higher than that of the original equipment. This method can also be used to optimize other parameters and solve mult-objective optimization problem. It opens up a new way to improve the intelligence of AWCC design.