Due to manufacturing process deviations, some parameter values of chips have large discreteness. In order to improve the stability and reliability of electronic systems, this paper designs a multi parameter low correlation clustering selection (MLCS) algorithm to enhance chip consistency. The algorithm first calculates the Spearman rank correlation coefficient between parameters, selects test parameters with low correlation for screening, improves the efficiency of selection, and then uses 1D K-means method to perform 3-level clustering on multiple parameters. Based on their classification results, chips concentrated in the cluster center are selected. The experimental results show that the algorithm can achieve automatic screening of test chips. The parameter values of chips located in the middle cluster center are all around the mean, with no deviation of one variance, and the classification boundary is clear. The clustering effect is not limited by the number of screening parameters; 894 samples were screened according to 2 parameters, and the scatter plot showed significantly better results than conventional 2D fuzzy clustering and 2D K-means algorithm; The time taken is about 0.04 seconds, while the fuzzy clustering algorithm takes over 12 seconds. This algorithm has good adaptability and can effectively select chips with multiple parameter values that are close to the mean.