Analysis of the evolution of modern Chinese history based on data mining

Abstract

Abstract In this paper, data mining is proposed to study the evolution of China’s modern history by addressing the problem of incomplete content of the evolution process. The data mining technique mainly preprocesses the data set of Chinese modern history by logistic regression algorithm, and its purpose is to detect the accuracy of the data so as to provide accurate and high-quality data for the data mining process. The process of visualization using information related to the evolution of modern Chinese history and the influence of modern Chinese historical events is applied to the visualization analysis, and the final influence of the evolutionary development of modern Chinese history is obtained and saved to the database by weighting and summing the influence factors of modern Chinese historical figures. The logistic regression algorithm uses modern historical persons and things as input data, and the weights of modern historical persons and things are the predictions carried out by classification. The results show that the highest accuracy is 0.67 when the threshold value is set to 1. The logistic classification model predicts better for the case of weight type 2 of modern Chinese history people and weight type 6 of modern history things. This study makes a certain contribution to the study of modern history so that the study of modern history can gradually move toward completeness and objectivity.