College poor student accurate subsidy model based on LSTM neural network

A neural network and technology for impoverished students, which is applied in the field of precision funding models for impoverished students in colleges and universities, can solve the problems of rare research reports, the precise identification of proposed funding objects needs further research, and the slow convergence speed of BP network, etc., so as to improve fairness and efficiency Effect

A neural network and technology for impoverished students, which is applied in the field of precision funding models for impoverished students in colleges and universities, can solve the problems of rare research reports, the precise identification of proposed funding objects needs further research, and the slow convergence speed of BP network, etc., so as to improve fairness and efficiency Effect

CN112102135APending Publication Date: 2020-12-18CHONGQING BUSINESS VOCATIONAL COLLEGE
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  • College poor student accurate subsidy model based on LSTM neural network
  • College poor student accurate subsidy model based on LSTM neural network
  • College poor student accurate subsidy model based on LSTM neural network

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Experimental program
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Effect test

Embodiment

[0101] 1. Data collection:

[0102]The reason why the artificial neural network can simulate the human brain for accurate classification and recognition is that it requires a large amount of data to train and test the neural network, so that the neural network model can classify unknown data patterns. As for the funding work of poor students in colleges and universities, it is necessary to analyze and predict the poverty level of poor students to determine the level of funding they should receive. The consumption data in the all-in-one card can directly reflect the economic status of the students, while the national bursary has the widest range of funding objects, and the formed data is relatively rich, which is suitable for the training and testing of the ANN model. In the present invention, the consumption records of the student campus card of Chongqing Business Vocational College in 2019 are collected, and the attached image 3 Shown are some student consumption records, a...

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Abstract

The invention discloses a college poor student precise subsidy model based on a long short-term memory (LSTM) neural network, which constructs the college poverty student precise subsidy model based on the LSTM neural network, analyzes the number of hidden layer neural units of the LSTM neural network through a data experiment, trains a relationship between an optimization algorithm and a povertystudent subsidy level recognition rate, and structure and parameter optimization is carried out on the college poor student accurate subsidy model based on the LSTM. The accurate subsidy model can identify the national college subsidy level according to the college poverty student consumption data, provides an intelligent quantification tool for accurate identification and classification of college poverty student subsidy, can reduce human interference factors, and has innovative significance for college student subsidy and learning assistance work.

Description

technical field [0001] The invention relates to an accurate subsidy model for impoverished college students based on LSTM neural network. Background technique [0002] The funding system for impoverished students in colleges and universities is an important part of national education poverty alleviation. With the rapid development of social economy, the funding system for impoverished students in colleges and universities is also facing some basic and common problems, and the identity identification of poor students has always been the foundation and difficulty of funding work At this stage, there are mainly problems such as simple identification basis, interference from human factors, one-sided reference standards, and difficulty in verifying the authenticity of quantitative indicators. At this stage, the research on precision funding for education mainly focuses on the precise identification of funding targets. The current research results are mainly divided into the foll...

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Application Information

Patent Timeline
18 Dec 2020
Publication
CN112102135A
IPC
G06Q50/26; G06K9/62; G06N3/04; G06N3/08
CPC
G06Q50/26; G06N3/049; G06N3/084; G06N3/045; G06F18/2415
Inventors
周俊