Abstract: Aiming at low prediction accuracy of wireless network traffic data, a classification prediction model named BABP (Bat Algorithm optimized Back Propagation), which based on Back Propagation (BP) neural network optimized by Bat Algorithm (BA) was proposed. By using bat algorithm to optimize the initial weights and thresholds of BP neural network model, a new neural network model based on bat algorithm optimization was constructed. Compared with the neural network model optimized by traditional Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), the proposed BABP model is better than GABP (Genetic Algorithm BP) model and PSOBP (Particle Swarm Optimization BP) model in classification prediction and stability of wireless network traffic data; at the same time, regardless of the number of iterations, BABP has faster convergence speed than GABP and PSOBP algorithms. The experimental results show that BABP model has more advantages than GABP and PSOBP models in prediction accuracy, optimization speed and model stability.