Abstract: An integrated model combining linear regress(LR) and supervised distributed neural networks(SDNN) based on the features of coke oven flue temperature is proposed.Progression is used to(analyze) the properties of flue temperatures,and rules of selecting typical regenerators are proposed.(LR models) with one variable,two variables and twelve variables are built and rationally integrated to map the linear relationship between flue temperature and top of regenerators' temperature.At the same time,after supervised clustering the samples,SDNN models are employed to synthesize the outputs of(every) sub-(network).The flue temperature is obtainted through the expert coordinator which is used to coordinate the outputs of LR and SDNN.The running results of the models validate the method.