Research on the effect of poverty alleviation in mountainous areas based on mathematical models

This paper takes the Qinba Mountains in Sichuan as the research object, analyzes the overall situation of industrial poverty alleviation, combines stratified sampling and cluster sampling, designs a research plan, and conducts field research. Then, through the comparative analysis of four classic machine learning algorithms, according to the model classification accuracy, the GBDT algorithm model of the factors influencing the effectiveness of industrial poverty alleviation was selected, and the importance of the influencing factors was evaluated and studied. From this, the main problems existing in industrial poverty alleviation in Qinba Mountains of Sichuan are analyzed and targeted suggestions are put forward.