A Deep Learning-Based Approach for Relative Poverty Identification and Classification Prediction

By predicting and classifying relative poverty, we can spot and tell the difference between potentially impoverished groups early on. This allows for early intervention and efficient resource allocation, aiding long - term poverty governance. Given the lack of algorithmic research in relative poverty identification using multi - year data, this paper proposes the RP - DCSA model. It blends deep learning (DNN) with the interpretable SHapley Additive exPlanation (SHAP) model. The 2020 China Family Panel Studies (CFPS) survey data form the research base. Spearman correlation coefficients are applied for feature selection to eliminate redundant ones. Next, the DNN - based RP - DCSA model is built and compared experimentally with LR, RF, etc. Finally, SHAP is used for interpretable analysis to identify key features affecting relative poverty classification and assess their impact on results. The RP-DCSA model achieves an 89.55% classification accuracy on the CFPS2020 dataset, outperforming other algorithms in various indicators.