The accurate forecasting of agricultural futures prices is critical for ensuring national food security. Therefore, this study proposes a text‐based deep learning forecasting model. This model first uses the ChineseBERT + a text convolution neural network to classify Weibo text and obtain a raw sentiment index. Then, complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, correlation coefficient, and sample entropy are combined to decompose and reconstruct the raw sentiment index and obtain a denoised sentiment index. Subsequently, the neural basis expansion analysis with exogenous variables is improved by designing a weight coefficient and Optuna is used to optimize the designed weight coefficient and the hyperparameters. Finally, the SHapley Additive exPlanations value is used to increase the interpretability of prediction results. Corn futures prices for the Dalian Exchange are used in forecasting to validate the accuracy and stability of the proposed model. Experimental results show that the proposed denoising sentiment index contributes more to the improvement of predictive model performance than the raw sentiment index. The proposed text‐based deep predictive model demonstrates strong predictive ability for prediction horizons of 30 and 60 days. SHapley Additive exPlanations value analysis shows that the three features with greater effects on corn futures prices are as follows: “Corn Spot Price of Zhengzhou market,” “CBOT_corn_futures_price,” and “Pork futures price.”

中文翻译：

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使用改进的神经基础扩展网络进行基于文本的玉米期货价格预测

农产品期货价格的准确预测对于保障国家粮食安全至关重要。因此，本研究提出了一种基于文本的深度学习预测模型。该模型首先使用ChineseBERT + 文本卷积神经网络对微博文本进行分类并获得原始情感指数。然后，结合自适应噪声的完全集成经验模态分解、变分模态分解、相关系数和样本熵，对原始情感指数进行分解和重构，得到去噪情感指数。随后，通过设计权重系数改进带有外生变量的神经基础扩展分析，并使用Optuna对设计的权重系数和超参数进行优化。最后，使用 SHapley Additive exPlanations 值来提高预测结果的可解释性。采用大连交易所玉米期货价格进行预测，验证了模型的准确性和稳定性。实验结果表明，所提出的去噪情感指数比原始情感指数对预测模型性能的提高贡献更大。所提出的基于文本的深度预测模型在 30 天和 60 天的预测范围内表现出强大的预测能力。SHapley Additive exPlanations价值分析表明，对玉米期货价格影响较大的三个特征分别是“郑州市场玉米现货价格”、“CBOT_corn_futures_price”和“猪肉期货价格”。