Abstract: This study addresses the challenges of measuring and evaluating relative poverty by introducing a comprehensive evaluation model based on the Analytic Hierarchy Process (AHP)-entropy method and BP neural networks. A multidimensional evaluation index system was constructed through expert consultation and literature review. The AHP-entropy method was then employed to determine the weights of the evaluation indicators, ensuring objectivity and scientific validity. Additionally, the BP neural network model was integrated to leverage self-learning and adaptive mechanisms for efficient and accurate poverty assessment. Empirical analysis shows that the model maintains a calculation error within 3.9%, demonstrating high precision and wide applicability. This research provides a novel approach that combines qualitative analysis with quantitative evaluation, offering a practical tool for governmental agencies to design effective poverty alleviation strategies. Moreover, the model opens new pathways for future research in regional poverty assessment, especially in enhancing cross-cultural adaptability and advancing intelligent evaluation models.

Xiaohua Cai, Ya Zhao, Lijia Chen, Juan Huang and Yang Xu. “Using Combined Weighting and BP Neural Networks for Relative Poverty Measurement and its Evaluation”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.10 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161018

@article{Cai2025,
title = {Using Combined Weighting and BP Neural Networks for Relative Poverty Measurement and its Evaluation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161018},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161018},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {10},
author = {Xiaohua Cai and Ya Zhao and Lijia Chen and Juan Huang and Yang Xu}
}