Artificial neural networks (ANN) have been applied in many instances successfully in which conventional mathematical modeling technologies are not accurate or capable, because of its capability of nonlinear analysis. But its routine training algorithms such as BP or other gradient algorithms always result in very slow convergence and easily getting stuck in a local minimum. Initial connection weights, learning parameter, inertial weight and networks structure are the factors that affect the accuracy of prediction .In this paper, the practice of Particle Swarm Optimization(PSO) algorithm optimizing neural networks was presented. The advantage of the PSO over many of the other optimization algorithms is its relative simplicity and quick convergence. The study focused on training the connection weights between the neurons in different layers and structure of BP neural networks through PSO algorithm in order to provide a new approach to determine the optimum structure system of high-rising buildings. The result indicates that the accuracy and convergence velocity processed by this method is much better than that only BP algorithm adopted.