Abstract
Farmers' Professional Cooperatives have been increasingly recognized as organizational forms facilitating economic development, agricultural modernization, and rural social capital construction as part of China's national rural revitalization strategy. Based on a big data study method, the study estimates the performance trajectory of FPCs and their impacts on social capital construction, income increase, poverty alleviation, agricultural restructuring, and employment in urban-rural areas. A combined approach of the Back Propagation Neural Network and the Mayfly Optimization Algorithm was put forward in an attempt to promote feature selection and accuracy of prediction for the intricate and multi-layered socioeconomic case. Big data and the proposed predictive model will be compared by the proposed study using the data of 820 members in 14 cooperatives. The outcome reveals that the suggested model achieved a 93.4 % success rate, a 26.4 % increase in earnings, an 18.7 % rise in technology uptake, and a 31.2 % rise in training attendance and engagement. Among cooperative members, descriptive statistics demonstrated a significant improvement in household income, market access, training engagement, and the application of technology. To facilitate generalization, the model was trained on 70% of the dataset, tested on 15% of the dataset, and validated on the other 15% through the use of cross-validation methods. The proposed model was found to be more accurate than traditional models with a 93.4% correctness rate and an RMSE value of 2.13. Other significant factors determining performance in cooperatives, including farm size, years of experience, and education, were also discovered by the model. FPCs strategically facilitate resource concentration, implementation of policies, and rural institutional integration along with enhancing farmers' economic welfare, as revealed by the findings.
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