In order to improve the accuracy of NOx emission concentration prediction of the coal-fired boiler and more accurately monitor the NOx pollution, this paper proposes a prediction method based on the least squares support vector machines (LSSVM) and the improved particle swarm optimization (PSO). According to LSSVM forecasting theory as well as the uncertainty of LSSVM parameter selection, an improved PSO algorithm to optimize the parameters of the model is used, a model of NOx emission characteristics is established, and the prediction results are compared with the results of other two methods simultaneously. Results indicate th at LSSVM is an effective modeling method which has higher fitting degree; the combination of improved PSO and LSSVM can improve the prediction accuracy and the generalization ability, and LSSVM is superior to the other two parameter optimization algorithms in the NOx emissions concentration forecast.