本文对基尼系数的应用领域进行了推广。理清了基尼系数定义上存在的分歧，严格定义了洛伦兹曲线，并以此为基础编写了计算基尼系数的MATLAB程序。用蒙特卡罗方法分别生成了多组单峰和双峰数据，比较它们的变异系数和基尼系数的变化趋势，研究变异系数和基尼系数衡量一组数据偏离平均分布的程度的能力差异，构造了能区分它们的衡量能力倾向的分布。通过研究255组学生成绩数据的变异系数和基尼系数，发现了它们衡量数据的不平均程度的区别。

This paper generalizes the application field of Gini coefficient. The differences in the definition of the Gini coefficient are clarified, the Lorentz curve is strictly defined, and a MATLAB program for calculating the Gini coefficient is written based on this. The Monte Carlo method was used to generate multiple sets of unimodal and bimodal data respectively, to compare the variation trends of their coefficients of variation and Gini coefficients, and to study the differences in the ability of the coefficients of variation and Gini coefficients to measure the degree to which a set of data deviates from the average distribution. Distributions that measure aptitude that can distinguish them. By studying the coefficient of variation and Gini coefficient of 255 groups of student achievement data, the difference between the two coefficients measuring the degree of unevenness of the data was found.