Aiming at the problems of traditional BP neural network that are greatly affected by the initial weight threshold and easily fall into local extremes, and the standard beetle antennae search algorithm has poor local search ability and low optimization accuracy. Therefore, an adaptive step factor chaotic beetle swarm algorithm is proposed to optimize the BP neural network classification model. Optimize the local search ability of the beetle antennae search algorithm by increasing the beetle population and introducing an adaptive step size update strategy, so that the algorithm jumps out of the local optimum and improves the calculation accuracy of the algorithm; Logisitic chaotic mapping is used to generate new individuals, and individuals with poor performance are replaced, which enhances the global search effect. In order to improve the classification effect of the BP neural network on the minority classes in the unbalanced data set, the SMOTE algorithm is used to process the unbalanced data set. The improved Beetle Antennae Search and Back Propagation Neural Network (IBAS-BPNN) classification model is established to optimize the initial weight and threshold in the BP neural network to improve the accuracy of the BP neural network classification model. In order to verify the performance of the classification model, the improved BP neural network classification model is compared with other six typical classification algorithms. The experimental results show that the average classification accuracy of the IBAS-BPNN classification model is higher than other algorithms. The improved chaotic beetle swarm algorithm has strong generalization ability, good robustness, and certain advantages.