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In this paper, an approach based on data-driven-Compensatory Fuzzy Neural Network (CFNN) is proposed application on a wastewater treatment process. CFNN is a hybrid system combining with compensatory fuzzy logic and neural network. The network has a higher fault tolerance and more stable system. The 98 sets of data are selected as training data, the other 20 sets as testing data to test the validity and generalization. Traditional Back Propagation Neural Network (BPNN) and CFNN approaches are used to establish BOD5(The five-day biochemical oxygen demand) soft-sensing model of a wastewater treatment process. The simulation with the actual industry data shows that the CFNN method obtains higher precision models than the traditional BPNN method. It is sure that the former model is more suitable for practical engineering applications. However, the forecast errors are larger at the volatile point of actual curve using the two methods. Choosing an appropriate compensatory degree can improve the efficiency of the compensatory learning algorithm.