Density peak clustering（DPC）algorithm can find non-spherical clusters and other advantages.However

local density and the nearest neighbor distance computation are easy to ignore the correlation between samples

and the clustering effect of the algorithm is poor on high-dimensional dataset.To solve these problems

a DPC algorithm based on sub-cluster fusion and linear discriminant analysis（SCF-LDA-DPC） is proposed.Firstly

the weighted Gaussian kernel density estimation function is constructed by introducing Pearson correlation coefficient between samples to calculate local density.Secondly

a seed cluster fusion strategy is designed to avoid data misallocation and optimize fault tolerance of algorithm.Finally

LDA algorithm is introduced to reduce the dimension of high-dimensional data to improve the robustness and accuracy of DPC algorithm.The experimental results of multiple datasets show that the SCF-LDA-DPC algorithm is superior to other excellent algorithms in clustering precision and performance.