机器学习算法能够处理高维和多变量数据，并在复杂和动态环境中提取数据中的隐藏关系，在注塑件尺寸预测中具有很好的应用前景。注塑件尺寸预测系统的性能取决于机器学习算法的选择，然而，传统的机器学习算法在实际应用中不能达到很好的预测效果。本文提出一种基于Stacking集成学习方法的融合模型，采用优化的特征选择方法，建立模型时通过对比不同的Stacking学习器组合方式，组合多种类型的学习器，从而得到预测性能最佳的模型。该模型在注塑件尺寸预测方面的性能较传统模型有了很大的提升，同时模型预测结果可根据特征解释回溯到实际生产中，为制造工艺和工序的优化提供决策指导。

Machine learning algorithms can handle high-dimensional and multivariate data and extract hidden relationships in data in complex and dynamic environments, which has a good application prospect in injection molding part size prediction. The performance of injection molding part size prediction system depends on the choice of machine learning algorithm, however, the traditional machine learning algorithm can not achieve good prediction effect in practical application. In this paper, a fusion model based on Stacking integrated learning method is proposed, and the optimal performance model is obtained by comparing different Stacking learner combinations and combining multiple types of learners. The performance of the model in injection molding parts size prediction is greatly improved compared with the traditional model, and the model prediction results can be explained back to the actual production according to the characteristics, providing decision guidance for the optimization of manufacturing processes and processes.