AUXILIARY EVALUATION SYSTEM DESIGN FOR TARGETED POVERTY ALLEVIATION

Targeted poverty alleviation refers to a poverty control method that uses scientific and effective procedures to accurately identify, assist and manage the poverty alleviation objects according to the environment of different poverty-stricken areas and the situation of different poor farmers. However, in the implementation of targeted poverty alleviation policy, whether the applicant is a poor household mainly depends on the subjective judgment of grass-roots poverty alleviation staff and the historical data analysis model of previous years. In such a situation, it is inevitable that there are some phenomena of pretending to be poor households. Because this method relies too much on people's subjective judgment, a personal economic level prediction model based on data analysis and machine learning technology to help poverty alleviation workers identify poor households came into being. The model can effectively solve the defects of traditional methods.