This study considers comprehensively the various factors of grain production yield and optimizes primary BP neural network weights using the improved Particle Swarm Optimization (PSO) algorithm, then establishes a prediction model suitable for prediction of small sample grain yield. The experiment proves that this model has higher prediction precision and greater fitness than grain yield prediction model based on classical BP neural network and PSO-BP neural network.