Evaluation of agricultural tourism competitiveness based on improved genetic neural network

This study proposes a new evaluation method for agricultural tourism competitiveness based on an improved genetic neural network model. By combining a number of key indicators, such as the number of tourists, tourism income, and agricultural product consumption, a comprehensive evaluation system is constructed, and a weighted scoring method is used to evaluate the competitiveness of different regions. The results show that the improved genetic neural network model is superior to the traditional method in forecasting accuracy and stability, especially in multidimensional data processing and nonlinear relationship modeling. In addition, a detailed analysis of the competitiveness scores of each region is carried out, and evidence-based policy recommendations are proposed to enhance the competitiveness of agricultural tourism in each region, including the improvement of infrastructure construction, agricultural consumption, and ecological protection. The indicator weights are determined using expert evaluation and the analytic hierarchy process (AHP) to ensure scientific validity. The research results provide scientific evaluation tools and practical strategy support for the development of agricultural tourism, which has high application value and academic significance.