To address the problem that chemical oxygen demand (COD) is difficult to be measured on line during sewage treatment, this study proposes a soft sensing model based on a radial basis function (RBF) neural network. First, the process variables related to COD are selected as input variables by using the measured data of a sewage treatment plant. Second, the soft sensing model of COD in effluent is built on the basis of an RBF neural network. The center value, width value, and weight of the RBF neural network are optimized by an adaptive genetic algorithm improved sparrow search algorithm (AGAISSA). The accuracy of the soft sensing model is ensured by improving the sparrow position update formula and introducing the adaptive crossover and mutation operation in the genetic algorithm. Finally, the soft sensing model based on the RBF neural network is applied to the measured data of a sewage treatment plant for verification. The results show that the AGAISSA optimized RBF neural network model can accurately predict the COD in effluent and has high prediction accuracy.