An Elman neural network optimized by improved beetle antennae search algorithm was proposed to improve the fault diagnosis accuracy of rolling bearing of civil aviation engine.Aiming at the defects of beetle antennae search such as precocity,the centroid position and step update operation of beetle were improved.Meanwhile the learning rate,weight and threshold of Elman network were optimized by improved algorithm.The improved beetle antennae search algorithm optimization Elman neural network was used to diagnose rolling bearing fault and normal state,and the influence of Elman network delay vector scale factor on fault diagnosis rate of bearings was analyzed.In order to verify the validity of IBAS-Elman model,the beetle antennae search algorithm,firefly algorithm and genetic algorithm were used as comparison algorithms.The experimental results show that the accuracy of the improved beetle antennae search algorithm is better than that of other comparison algorithms.