Accurate Modeling of Random Forest Algorithm in Predicting Rural Consumer Behavior

With the booming rural economy and the deepening of digital transformation, accurately understanding the behavioral patterns of rural consumers has become a key issue that companies and policymakers need to address urgently. However, the scarcity and complexity of data in rural areas pose a huge challenge to traditional prediction models, resulting in unsatisfactory prediction accuracy. In order to overcome this problem, this paper uses the random forest algorithm for modeling and analysis to improve the accuracy of rural consumer behavior prediction. This paper first outlines the background and importance of rural consumer behavior research, and reviews current related research and its shortcomings. On this basis, this paper elaborates on the data collection and preprocessing process, as well as the specific application method of the random forest algorithm in predicting rural consumer behavior. The experimental results show that the random forest algorithm shows high accuracy and fast prediction time in predicting rural consumer behavior. The optimized prediction time is between 0.3 s and 0.7 s, which has significant advantages over traditional prediction models and provides a new high-precision model for predicting rural consumer behavior.