Using Big Data and Machine Learning to Optimize Agricultural Resource Allocation and Ecological and Economic Benefit Evaluation

This study explores the optimization methods of agricultural resource allocation and their impacts on ecological and economic benefits through big data and machine learning techniques. By combining Random Forest (RF) and Genetic Algorithm (GA), the study analyzes the effects of different agricultural resource allocation schemes on ecological and economic outcomes. The results show that precise resource allocation not only enhances agricultural productivity but also effectively reduces environmental burdens. Based on data analysis, this study proposes several optimization pathways, such as improving technological levels, promoting precision agriculture, and implementing multi-objective optimization strategies.The findings highlight the significant role of big data and machine learning in improving agricultural resource allocation efficiency, promoting ecological protection, and advancing sustainable agricultural development.