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In order to predict the water quality of sewage treatment, a NW multi-layer forward small world artificial neural networks soft sensing model is proposed for the waste water treatment processes, regarding the characteristics of multivariable, nonlinear, time-varying and time lag in the treatment process. The input and output variables of the network model were determined according to the waste water treatment system. The multi-layer forward small world artificial neural networks model was built, and the hidden layer structure of the network model were studied. The waste water treatment process experiments and the training and simulation of the soft sensing model based on the experimental data were conducted. The results show that compared with the same size of the multilayer feedforward neural network, the small world neural network has a higher precision and convergence speed, and provides a new method for the real-time prediction of the wastewater.