针对大豆期货价格预测中特征选择缺乏因果验证、可解释性不足的问题，构建融合事前特征筛选与事后可解释性的集成预测框架。采用TreeSHAP与传递熵进行二阶段特征筛选，实现统计重要性与信息因果性的双重验证；并设计门控循环单元、随机森林、时序卷积网络3组功能互补的子模型群，运用进化算法与强化学习结合的权重优化策略动态调整集成权重；建立全局归因与局部解释的协同可解释性框架。基于2000～2023年月度数据的实证研究表明，测试集的平均绝对百分比误差低于所有对比方法。可解释性分析从战略、战术、盘面3个层次揭示多维驱动机制，为政府储备调控、企业供应链管理和机构投资配置提供决策依据。

This study proposes an ensemble soybean futures price forecasting framework that combines ex-ante feature selection with ex-post interpretability to address the lack of causal verification in feature selection and insufficient interpretability. TreeSHAP and transfer entropy are employed together for two-stage feature selection to provide dual validation of statistical significance and information causality. Three functionally complementary sub-model groups, including gated recurrent units, random forest, and temporal convolutional network are designed, with an evolutionary algorithm combined with reinforcement learning to dynamically optimize ensemble weights. A collaborative interpretability framework combining global attribution and local explanation is established. Empirical research based on monthly data from 2000 to 2023 shows that the mean absolute percentage error on the test set is lower than that of all comparative methods. Interpretability analysis reveals the multi-dimensional driving mechanisms from three levels of strategy, tactics, and market trading conditions, providing decision-making support for government reserve regulation, enterprise supply chain management, and institutional investment allocation.