Using Combined Weighting and BP Neural Networks for Relative Poverty Measurement and its Evaluation

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.

@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},
  volume    = {16},
  number    = {10},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Xiaohua Cai and Ya Zhao and Lijia Chen and Juan Huang and Yang Xu},
  doi       = {10.14569/IJACSA.2025.0161018},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161018}
}