Poor student subsidization recommendation method based on school behavior data multidimensional analysis

Abstract

The invention relates to a poor student subsidization recommendation method based on school behavior data multidimensional analysis. Compared with the prior art, the method solves the defects that theaccurate recommendation of a poor student to be subsidized is difficult in realization. The method comprises the following steps that: obtaining historical behavior data; extracting the features of the historical behavior data; training a recommendation model; obtaining behavior data to be analyzed; extracting the features of the behavior data to be analyzed; and obtaining a recommendation result. By use of the method, on the basis of the school data generated by the student, the features of multiple dimensions are extracted, the features are used for establishing a classification model, anda poverty situation of the student can be accurately judged by virtue of the classification model, and a decision is made.

Description

Technical field [0001] The present invention relates to the technical field of big data analysis, and specifically is a method for recommending funding for poor students based on multi-dimensional analysis of school behavior data. Background technique [0002] The advent of the era of big data provides new ideas and technical support for the funding of poor students, and also brings new opportunities for universities to use big data to promote fast, convenient, efficient and precise funding. Using big data mining and analysis technology and mathematical modeling theory to help managers grasp the real behavioral patterns of students during school, discover "hidden poverty" and suspected "false identification" students, and achieve precise funding. [0003] At present, the precise funding of poor students in schools is still in the exploratory stage. There is no unified evaluation method in China, and there is no uniform delimitation standard for poor students in schools. The lack of...

AI Technical Summary

Problems solved by technology

[0003] At present, the precise subsidy for poor students in school is still in the exploratory stage. There is no unified evaluation method in China. There is no unified demarcation standard for poor students in school. The subsidy for poor students lacks systematic and standardized management. It is very cumbersome and causes a lot of waste of data resources

Although some technologies have put forward some viewpoints and ideas, none of them can meet the practical application or are difficult to realize. For example: the patent application document with the patent number 201710223971.6 and the patent name is the method for predicting students' poverty status based on data mining

Although it is aimed at analyzing the data of students in school, it directly uses the big data platform hadoop and spark, and the model uses random forest, and does not carry out targeted technical data classification for the data of students in school, so that the classification results are not accurate. ideal

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