Abstract
The college entrance examination application is a complex system project, which needs to collect many kinds of information. Aiming at the deficiency of the system research based on the analysis of the domestic mainstream platform, the reference system and Sina simulation system of college entrance examination application, the intelligent application system of college entrance examination application is designed based on big data technology. Considering the scores of examinees, the enrollment plan of colleges and universities, the enthusiasm of application, the prospect of professional development and other factors, the hardware structure of the intelligent filling system for college entrance examination is constructed. Through big data analysis and data mining, a large amount of real and valuable information for college entrance examination filling can be provided for the majority of examinees. It can be seen from the experimental verification results that the system fills in accurate results and has an ideal filling effect, which helps the candidates to apply for the ideal school and improve the admission rate.
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Acknowledgements
1. The “Thirteenth Five-Year Plan” Science and Technology Project of Jilin Province Education Department “Research on the Service System of Volunteer Filling for College Entrance Examination Based on Data Mining” (Project Contract Number: JJKH20180466KJ).
2. The 2017 General Planning Project of the “13th Five-Year Plan” of Educational Science in Jilin Province “Research and Practice of Senior High School Students’ Career Planning from the Perspective of the New College Entrance Examination Reform” (Project approval number: GH170348).
3. The 2017 Higher Education Scientific Research Key (Self-funded) Project of Jilin Province Higher Education Society “Research on the Decision Model of College Students’ Career Planning” (Project Number: JGJX2017C43).
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Guo, Sx., Lin, L. (2021). Research on Voluntary Intelligent Reporting System of College Entrance Examination Based on Big Data Technology. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-030-67871-5_10
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