A method combined ANN （artificial neural network） with PSO （particle swarm optimization algorithm） to optimize the processing parameters of QSn6.5-0.1 horizontal continuous casting has been put forward taking seven main casting technological factors as optimizing parameters

and ratio of accepted products as optimizing objective. Through the orthogonal experiment of the technical factors in the casting process

the mapping model of ANN was established based on the data from above experiment. Through optimizing the model by PSO

the optimized processing parameters have been given. A learning algorithm of subtractive clustering method （SCM） was used for radial basis function （RBF） network to get parameters RBF with the help of the least-square method （LSM）

and a tactics which alters the weight dynamically according to particle position and objective function values is applied to modify the PSO. Practical production verifies that it increases the ratio of accepted products from 56% to 71%.