Application of data mining based on Improved Ant Colony Algorithm in college students' employment and Entrepreneurship Education

Abstract

Abstract Under the background of the new era of economy, the employment and entrepreneurship education of college students has also ushered in new problems. In the construction of student employment and entrepreneurship data mining model, data solving efficiency and association rule mining play a key role in the performance of the model. How to optimize and improve the mathematical model and improve the accuracy of student employment and entrepreneurship data analysis needs further research and analysis. This paper constructs the mathematical model of ant colony algorithm, introduces the logical process in solving practical problems, provides setting suggestions for the value of algorithm parameters, and improves the solution efficiency. This paper improves the decision tree classification method and ant colony algorithm. The association rule mining based on ant colony has more perfect reference value. From the combination and interest analysis of College Students' employment and entrepreneurship content, it provides efficient, practical and useful information for the school. Finally, after constructing the prediction model, this paper analyzes the four factors affecting the choice of College Students' homework entrepreneurial intention, which are individual, family, school and society, and puts forward five measures to improve college students' Entrepreneurship and employment education.