In order to study the influence of clustering parameters on the regulation performance of production processes in the pattern-moving-based system dynamics description method, indexes describing dynamic regulation performance and product quality regulation performance are proposed, and the relationship between clustering parameters and regulation performance is analyzed and built. A pattern moving based modeling method of a class of production processes is introduced. A state feedback controller is designed by using the LMI method. A maximum-entropy clustering algorithm based on the particle swarm optimization(PSO) is proposed, and regulation performance of process control is defined and extracted. The relationship between clustering parameters and regulation performance is built by using a proposed classification neural network based on a covering algorithms, and generalization of the network is analyzed. Data of an actual sintering process is used for simulation experiments, and results demonstrate that the proposed method can be used to analyze and build the relationship between clustering parameters and regulation performance, which provides a basis for the selection of clustering parameters in actual production.