Research on Agricultural Economic Early Warning Based on Genetic Algorithm and SVM

Agriculture is unique in that the industry subject to a certain level of uncertainty due seasonal and other factors, its risk significantly higher than industries. Therefore, it necessary establish an appropriate financial early warning model predict, analyze, control risks. The article uses genetic algorithm support vector machine-based economic forecasting for agribusinesses adapt own pollutant weights practical application environment improve accuracy. This first train feature weight current data so are adapted prediction problem then this apply SVM training. It found 62.79% listed agricultural companies status, their development not optimistic. provides new ideas subsequent research on methods also expands boundaries theoretical system problems enriches experience evidence research.