Research And Implementation Of Agricultural Product Price Forecasting Model Based On Big Data

China has been an agricultural country since ancient times,and agriculture is the top priority of China.In recent years,China has successively introduced economic policies favorable to agriculture to promote the development of China's agricultural modernization,in order to regulate the sustained and steady development of China's agriculture.However,the random fluctuations in agricultural prices and their instability have led to obstacles to agricultural development in the country.Therefore,accurate prediction of agricultural product prices will be particularly important.On the one hand,accurate prediction of agricultural product prices can provide a reference for future economic policy formulation.On the other hand,it will contribute to the sustained and stable development of China's agricultural economy.This paper studies from the following aspects:1.Research on forecasting methods of agricultural product prices.Appropriate prediction methods can greatly improve the prediction accuracy.By comparing multiple prediction methods,the prediction of support vector regression is the best,but because of the limitations of such data processing,SDP-SVR(Support)Vector Machine Regressing based on Sample Data process),the model better solves the defects in data processing,and uses the support vector regression algorithm to greatly improve accuracy and stability.2.Simple processing of agricultural product price metadata.Every agricultural product has its own potential economic laws,and the factors affecting its price trend are also different.Therefore,metadata processing is particularly important.This article uses simple data processing to process metadata.Thereby indirectly improving the accuracy of price prediction.The results show that the SDP-SVR model has high prediction accuracy,reasonable and reasonable stability.3.Selection of independent variable factors in agricultural product price forecasting model.In actual research,agricultural product price forecasting should not only consider its own influencing factors,but also consider the influencing factors of its external environment.This article uses a number of external factors,such as supply and demand index,weather index,dollar exchange rate and other factors.4.Design and implementation of agricultural product price forecasting system.Using multi-tier architecture for software development,SSM framework for the overall design of the system architecture,including Spring framework for datatransmission layer development,Spring MVC framework for the normal realization of background services,MyBatis for data exchange between the front and back.