In order to solve the problem of determining the distribution of different types of objects in system faults，a method to determine the distribution of objects was proposed. Firstly，the characteristics of the system fault evolution process and object distribution were discussed. Secondly，the method flow chart and implementation process were given. Finally，an example was analyzed. The example studied the basic data matrix composed of 6 factors and 50 objects，and the maximum training set cross-correlation was 0.8，the test set cross-correlation was 1，and the optimal object label distribution (object distribution) was obtained. Finally，the advantages and disadvantages of the method were described. The analysis shows that the database for studying the evolution process is the object set. Methods based on UKSR，combined with K-means and mutual information methods，a randomly distributed object label set is constructed，and the criteria for the optimal object label set are proposed. The optimal object label set is determined through a loop when the correlation between object labels and object data is the largest. The label value of objects in the set is the optimal object distribution. The method overcomes the problem of unsupervised learning and nonlinear mapping. It is concluded that the method can classify the measured objects in the system fault evolution process under unsupervised and nonlinear conditions，and the distribution of class labels of all objects with evolution time. The disadvantage is that it can only be used to study the system fault evolution process represented by two-dimensional.