Abstract： Considering the nonlinearity of grain yield, the prediction model of support vector machine was proposed based on hybrid intelligent algorithm. To solve the problem that particle swarm optimization (PSO) was easy to fall into a local optimum, the hybrid intelligent algorithm (GAPSOAFSA) was proposed by combining the improved PSO and the artificial fish swarm algorithm (AFSA).The objective function value was quickly converged to the global optimal solution by the mutation cross within the swarm and the external competition mechanism. The global search ability of the algorithm was improved to obtain the optimal parameter combination of support vector machine. The support vector machine prediction model was used to predict Chinese grain yield, and the model correctness was verified by the experiments.The results show that the prediction model has good prediction result.
