构建基于神经网络算法的精准资助育人模型

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摘要：高校资助育人的关键在于精准识别目标群体。该文运用大数据与AI技术，通过神经网络算法构建贫困生等级预测模型，经训练与测试验证其准确性，相较于逐步线性模型，该模型提供更客观支撑，对高校实现精准资助及管理具有重要意义。

关键词：神经网络算法；精准资助；育人模型；逐步线性模型

doi：10.3969/J.ISSN.1672-7274.2024.11.003

中图分类号：TP 18                 文献标志码：A            文章编码：1672-7274（2024）11-000-03

Construction of a Precise Financial Aid and Educational Model

Based on Neural Network Algorithms

ZHANG Li

（Jiangsu Vocational College of Electronics and Information， Huaian 223003， China）

Abstract： The key to financial aid and educational support in colleges and universities lies in the precise identification of target groups. This paper utilizes big data and AI technology to construct a poverty level prediction model for students through neural network algorithms. The accuracy of the model is verified through training and testing. Compared with the stepwise linear model， this model provides more objective support and is of great significance for colleges and universities to achieve precise financial aid and management.

Keywords： neural network algorithms; precise financial aid; educational model; stepwise linear model

0   引言

随着高校资助工作的深入，资助育人理念日益深化，对精准资助提出了更高要求，其核心在于细化、公平化资助目标群体，以实现精准化、科学化的资助育人。（剩余3802字）