The traditional clustering process of multiparametric flow cytometry data analysis is complicated, nonautomated and timeconsuming. To overcome this limitation, an automatic clustering method based on kernel entropy analysis (KECA) is proposed. The feature vector with the greatest contribution to the Renyi entropy is selected as the projection direction to carry out the feature extraction. A classifier based on cosine similarity and the Kmeans algorithm is designed to get the label of each cell, and a method for determining the optimal number of clusters based on the angle of vectors is adopted. Experimental data of peripheral blood lymphocyte is processed, and the results indicate that the proposed method can realize automatically clustering with simple operation and the overall accuracy rate of clustering can reach 97%, which can improve the efficiency of cell analysis.