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A method for NOx prediction in SCR denitration based on the fusion of MiniBatchKMeans clustering and stacking model is proposed to address the complex characteristics of the denitration process of selective catalytic reduction (SCR) denitration system, such as non-linearity and multiple working conditions.. The method applies the MiniBatchKMeans clustering algorithm to the training set for work condition clustering and partitioning optimization, and establishes the stacking fusion framework prediction model (Stacking-XRLL) based on XGBoost, Random Forest, LightGBM and linear regression to achieve accurate NOx emission prediction under multi-variable work conditions in power station SCR systems. The modeling simulations and experiments were carried out with NOx emission data from the denitrification process of a power station SCR system in China. The results show that the Stacking-XRLL modeling method achieves an average prediction accuracy of 99% compared to the single modeling methods of the multilayer back propagation neural network(BP), long-short term memory neural network(LSTM) and gate recurrent unit neural network(GRU). The final combination of the established deep deterministic policy gradient (DDPG) reinforcement learning model enables the optimal control of the SCR denitrification process in a power station.