Research on the Assessment Method of College Students' Financial Difficulty Index in the Context of Big Data Based on AHP Algorithm

Abstract

The emergence of big data has allowed researchers to study people's financial struggles in greater detail. Among various kinds of difficulties, the financial barriers preventing low-income children from enrolling in higher education have received significant attention. Therefore, this paper uses the AHP algorithm and proposes a college student poverty index assessment method, aiming to provide a reference basis for the normal enrollment of children from low-income families by comprehensively analyzing various factors. This paper first constructs a hierarchical model to decompose the college student poverty problem into multiple influencing factors. Then, the AHP algorithm was utilized to calculate each factor's weights and consistency test, and each factor's relative importance was derived. As a result, each college student's poverty index was calculated using the comprehensive score method. The results show that the assessment method can reflect the degree of economic and social difficulties of college students from low-income families with better accuracy, providing a reliable reference value for promoting them in regular college enrollment. At the same time, the method can also be used in the government's formulation of poverty alleviation policies and the school's educational assistance work.