为了研究复杂系统，本文提出了介科学引导的深度学习建模方法（MGDL）。在基于同一系统演化数据构建样本数据集时，与传统深度学习方法有所不同，MGDL根据介科学理论，对复杂系统的主导机制及其通过竞争中协调原理的相互作用进行处理。然后将介科学约束纳入损失函数，以引导深度学习的训练建模。本文提出了两种添加介科学约束的方法。由于提供了基于物理原理的引导和约束，MGDL提高了模型训练过程的物理可解释性。本文使用一个鼓泡床建模案例对MGDL进行了评估，并与传统建模进行了比较。结果表明，在训练数据集规模较小时，基于介科学约束的模型训练在收敛稳定性和预测准确性方面具有明显优势。MGDL可广泛应用于各种构型的神经网络。本文提出的MGDL方法是一种在深度学习模型训练过程中利用物理信息的新方法，未来将继续对MGDL进行深入探索。

In this paper, we propose mesoscience-guided deep learning (MGDL), a deep learning modeling approach guided by mesoscience, to study complex systems. When establishing sample dataset based on the same system evolution data, different from the operation of conventional deep learning method, MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition (CIC) in mesoscience. Mesoscience constraints are then integrated into the loss function to guide the deep learning training. Two methods are proposed for the addition of mesoscience constraints. The physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are provided. MGDL was evaluated using a bubbling bed modeling case and compared with traditional techniques. With a much smaller training dataset, the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy, and it can be widely applied to various neural network configurations. The MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model training. Further exploration of MGDL will be continued in the future.