With the rapid development of big data and artificial intelligence, agricultural product price forecasting is evolving toward more intelligent and accurate approaches. However, such prices are affected by complex factors including natural conditions, market dynamics, and policy changes, resulting in strong nonlinearity and noise. To address the above challenges and achieve accurate agricultural price forecasts, this study proposes a hybrid framework that integrates a secondary decomposition algorithm with an improved Human Evolutionary Optimization Algorithm specifically tailored for the agricultural domain. The original price series is first decomposed using complete ensemble empirical mode decomposition with adaptive noise, and the high-frequency component is further processed using variational mode decomposition to enhance feature extraction. The improved optimization algorithm introduces Gaussian mutation and adaptive weights to optimize neural network parameters. Experiments on wheat, Chinese cabbage, and broiler chicken demonstrate that the proposed model significantly improves prediction accuracy, with determination coefficients increasing by 6.69, 8.87, and 6.43 percentage points, respectively. The results confirm the model’s effectiveness in reducing noise, capturing multi-scale features, and improving forecasting performance.
Wang et al. (Wed,) studied this question.