Analysis of Agricultural Market Trend Under Digital Economy by Using Grey Neural Network

With the vigorous development of digital economy, the agricultural market is facing new challenges and opportunities in the wave of informationization and digitalization. The purpose of this study is to realize the accurate analysis and prediction of agricultural market trends by using the model of Grey Neural Network (GNN) and combining the data advantages in the digital economy era. Through the deep mining and rational utilization of digital economic data, this study established a flexible and adaptable analysis framework, aiming to improve the scientific and accurate agricultural decision-making. Making full use of the advantages of GNN, through flexible neural network structure and grey relational degree calculation of grey system theory, modeling the complex relationship of digital economic factors in agricultural market. After reasonable selection and fine adjustment, the parameters of the model can better adapt to the characteristics of the agricultural market in the digital economy era, including variability and nonlinearity. The GNN model is applied to real-time agricultural market data, and the dynamic monitoring of agricultural market trends is realized. The prediction results of the model show satisfactory accuracy and stability, and there is a significant consistency with the actual market performance. Compared with the traditional support vector machine (SVM), this study found that GNN showed higher prediction accuracy in the trend analysis of agricultural market in the digital economy era, which further verified its value in agricultural decision-making. The results of this study provide a new and effective method for the analysis of agricultural market trends in the era of digital economy, and provide more scientific and reliable data support for agricultural decision makers.