In the wastewater treatment process, due to the complexity of the industry process, incomplete monitoring equipment and hostile working environment, it is difficult to achieve accurate and timely measurement of important effluent indices. To solve the problem, an ensemble adaptive soft sensor model based on semi-supervision learning was proposed. Firstly, Gaussian process regression (GPR) and Relevance vector machine (RVM) were used to establish a heterogeneous soft-sensor model. Then, the structure and parameters of the models  were optimized by using the moving window and the Kalman filter gain, respectively. Finally, the prediction performance and self-adaptability of the model were verified by experiments on a wastewater plant. The results demonstrate that the proposed method improves the prediction accuracy and self-adaptability of the soft sensor model.