Research on Automatic Estimation Method of College Students’ Employment Rate Based on Internet Big Data Analysis

  • Xiao-hui ZhangEmail author
  • Li-wei Jia
  • Fa-wei Zhou
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 301)


In order to solve the problem of large error and inaccuracy in employment rate estimation, an automatic employment rate estimation method based on Internet big data analysis is proposed. This method can be divided into four steps: Firstly, the data integration model based on XML middleware is used to select the sample data of employment rate estimation. Secondly, the decision tree C4.5 algorithm is used to classify the attributes of the sample data. Thirdly, the improved KPCA algorithm is used to extract the feature vectors of employment information and calculate the distance between the forecasted samples and all samples. Fourthly, non-linear mapping method is used to transform employment structure data into corner data, and grey theory is used to establish employment rate estimation model. The results show that the average employment rate estimation error of this method is 4.81% lower than that of the statistical method based on support vector machine.


Internet big data analysis Employment rate Estimation method 


  1. 1.
    Wang, S.: Based on large data analysis of disadvantaged groups in colleges and universities graduates employment situation in recent years. China Univ. Students Career Guide 2(17), 55–58 (2016)Google Scholar
  2. 2.
    Yang, H., Li, L.: Viewpoint on the employment rate of graduate students in agriculture and forestry. Heilongjiang Agric. Sci. 58(7), 78–82 (2017)Google Scholar
  3. 3.
    Yan, Y., Deng, F.: Employment situation and characteristics for college graduates—discovery from data of 42 colleges and universities in Chengdu. J. Southwest JIAOTONG Univ. (Soc. Sci.) 17(1), 1–8 (2016)MathSciNetGoogle Scholar
  4. 4.
    Yao, J., Chen, Y.: Employment status of graduates of food quality and safety specialty in West Anhui University. Anhui Agric. Sci. Bull. 22(6), 177–179 (2016)Google Scholar
  5. 5.
    Liu, H.: Verification method for the maximal employment rate of university. J. Guangdong AIB Polytech. Coll. 33(3), 48–53 (2017)Google Scholar
  6. 6.
    Zhao, X., Chen, X.: Clustering analysis based on graduates’ employment quality pluralistic evaluation. J. Xinyang Agric. Coll. 26(4), 149–151 (2016)Google Scholar
  7. 7.
    Zhang, R., Zhang, W., Wu, C.: Survey and analysis of employment status of graduates in mechanics—based on the statistic data of graduates in the Department of Engineering Mechanics at Dalian University of Technology during the past five years. China Univ. Students Career Guide 9(11), 59–64 (2016)Google Scholar
  8. 8.
    Kong, P., Huang, C., Lu, Y.: Employment statistics and Countermeasures for aquaculture graduates—taking Guangdong Ocean University as an example. Agric. Dev. Equipments 36(11), 38 (2016)Google Scholar
  9. 9.
    Lei, Y.: Further improve professional art school graduates’ employment quality evaluation system –thinking based on MyCOS company on employment-based data analysis of music schools. Guide Sci. Educ. 12(1), 167–169 (2016)Google Scholar
  10. 10.
    Huang, N.: On the employment situation and countermeasures of international Chinese education in application-oriented universities—a case study of Huangshan University. J. Huangshan Univ. 18(1), 118–122 (2016)MathSciNetGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Henan Medical CollegeZhengzhouChina

Personalised recommendations