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Aiming at the problem that the cement production process is inherently affected by uncertainty, time delay, and strong coupling among variables, this paper proposed a novel soft sensor of free calcium oxide in a cement clinker. The model utilizes a dual-parallel integrated structure with an optimized integration of one-dimensional convolutional neural networks, long and short-term memory networks, graphical neural networks, and extreme gradient boosting. The proposed model can mitigate the risks associated with overfitting while incorporating the strengths of each individual model and excels in extracting both local and global features as well as temporal and spatial characteristics from the original time series data, ensuring its stability. The experimental results demonstrate that this dual-parallel integrated model exhibits superior robustness, predictive accuracy, and generalization capabilities when compared to single models or enhancements made to other deep learning algorithms. [ABSTRACT FROM AUTHOR]