Construction of Agricultural Economic-Level Prediction Model Based on GWO-SVM Algorithm

In response to the inability of traditional economic predication models to accurately predict agricultural economy, a method based on improved grey wolf optimization algorithm combined with support vector machine is proposed to establish an agricultural economy predication model. By analyzing the advantages and disadvantages of the grey wolf optimization algorithm, particle swarm optimization algorithm is used to improve the grey wolf optimization algorithm and enhance its search ability. Combined with support vector mechanism, a grey wolf optimization algorithm support vector machine prediction model is established. The results indicate that the proposed model can effectively perform economic forecasting, with a fast convergence speed and the ability to find the global optimal solution after 150 iterations. Overall, the research proposed model can raise the accuracy and efficiency of economic predication.