Research On Identifying Financial Aid For Poverty Students In Higher Vocational Colleges Based On Association Rules

Now the state continues to implement the policy of expanding recruitment in higher vocational colleges,emphasizing that vocational colleges should help with the policy of targeted poverty reduction and alleviation,so that more young people have a skill to realize their value in life.Under the current policy of precision poverty alleviation strategy of poverty alleviation,the identification of poor students in colleges and universities has become one of the important work of schools.Linxia Modern Vocational College is a full-time general vocational college approved by the Gansu provincial government,which offers 18 majors such as pre-school education,midwifery and computer application technology.In order to support the development of poor areas,comprehensively promote education poverty alleviation,win the fight against education out of poverty,colleges mainly recruit students from poor areas,which makes it more difficult for poverty students identify in school.At present,colleges and universities mainly identify poor students through the "Family Situation Questionnaire" submitted by students and proof of poverty family,among which there are many problems and shortcomings.In order to ensure that the financial support work of poor students can be carried out accurately and efficiently,it is necessary to have a scientific and feasible approach to improve the current identification.Therefore,the paper has very realistic research value for the study of the problem of poor students' financial aid.Firstly,the paper studied the classic Apriori algorithm,combined with the current national criteria and policies of the identification of poverty students,pre-processe and mine the information data of students applying for poverty identification,retain the association rules exceeding the minimum confidence,analyze and evaluate association rules which meet the conditions.It provides a scientific basis for the accurate,reasonable discrimination and evaluation of poverty students in colleges and universities.Secondly,when the mining campus card data with the association rules,the scale is large and the data is many,so the paper improves and optimizes the Apriorialgorithm,and analyzes the performance of the improved algorithm.Only one scan operation of the database,will not produce a large number of candidate sets,the use of segmentation method to improve the efficiency of the algorithm,to identify potential poor students,to facilitate the college funding center and college leaders accurately and rationally assess poor students and grant grants.Finally,the consumption data of Linxia Modern Vocational College 2016-2018 campus card were used to study the correlation between students' consumption habits and the declaration of poverty students.Process the data on the platform of MySQL community server 5.6,pre-school students selected data are the research samples.The study of student poverty indicators demonstrates the application of the improved Apriori algorithm in the analysis of poverty indicators.Through the analysis and comparison of the association rules generated by data mining,the proposed method of identifying poverty students based on the association rules has a certain guiding effect on the identification of poverty students in colleges and universities;Based on the campus card consumption data to optimize the identification method of poor students in colleges and universities,we can excavate the potential poor students in colleges and universities,and have a certain complementary effect on the identification of poor students.