Many application scenarios require that the number of each category is relatively balanced, and the traditional fuzzy C-means (FCM) clustering algorithm cannot achieve this function. For this reason, we first design a label distribution entropy by using the label information , which can evaluate the balance degree of clustering. Then, the label distribution entropy and the square loss between the fuzzy membership matrix and the label matrix are simultaneously introduced into the traditional FCM, and then a fuzzy C-means balanced clustering method based on the regular label distribution entropy (${\textFC