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A recommendation method for financial aid for poor students based on multidimensional analysis of school behavior data

A multi-dimensional analysis and recommendation method technology, applied in the field of big data analysis, can solve problems such as unsatisfactory classification results, waste of data resources, cumbersome funding work, etc., and achieve high accuracy of recommendation results, small data volume requirements, and accurate information acquisition Effect

Active Publication Date: 2021-11-12
HEFEI CITY COULD DATA CENT
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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

Method used

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  • A recommendation method for financial aid for poor students based on multidimensional analysis of school behavior data
  • A recommendation method for financial aid for poor students based on multidimensional analysis of school behavior data
  • A recommendation method for financial aid for poor students based on multidimensional analysis of school behavior data

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Embodiment Construction

[0083] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0084] Such as figure 1 As shown, the method for recommending financial aid for poor students based on multidimensional analysis of school behavior data in the present invention includes the following steps:

[0085] The first step is the acquisition of historical behavior data. Obtain historical behavior data of past students in multiple dimensions. Historical behavior data includes past students' family economic data, campus card consumption data, student performance data, and library borrowing data. For learning and living conditions, since the establishment of the recommendation model in the present invention is based on the characteristics extracted from behavioral data for construction training, the accurate selection of basic ...

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Abstract

The invention relates to a recommendation method for impoverished students based on multidimensional analysis of school behavior data, which solves the defect that it is difficult to accurately recommend impoverished students to be aided compared with the prior art. The invention comprises the following steps: acquisition of historical behavior data; feature extraction of historical behavior data; training of recommendation model; acquisition of behavior data to be analyzed; feature extraction of behavior data to be analyzed; Based on the in-school data generated by students, the invention extracts features of multiple dimensions, uses these features to establish a classification model, and accurately judges the poverty situation of students and makes decisions by means of the classification model.

Description

technical field [0001] The invention relates to the technical field of big data analysis, in particular to a method for recommending financial assistance for poor students based on multidimensional analysis of school behavior data. Background technique [0002] The advent of the era of big data has provided new ideas and technical support for the funding of poor students, and has also brought new opportunities for colleges and universities to use big data to promote fast, convenient, efficient and accurate funding. Using big data mining and analysis technology and mathematical modeling theory to help managers grasp the real behavior patterns of students during school, discover "hidden poverty" and suspected "false identification" students, and achieve precise funding. [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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/17G06F16/2458G06Q50/20
CPCG06Q50/205G06F16/2462G06F16/2465
Inventor 孙浪施星靓刘胜军李晓洁孟虎李海松
Owner HEFEI CITY COULD DATA CENT
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