复合材料螺栓连接强度预测中，深度学习黑箱模型虽然能够快速准确地学习映射关系，但缺乏内在物理解释，难以理解模型的内部决策逻辑机制，可信度与普适性差。在融合复合材料物理规律约束与非线性辨识约束的基础上，提出一种多约束辨识的物理信息神经网络（MCI-PINN）。首先，物理规律的约束下使用复合材料螺栓连接挤压强度工程估算公式；其次，以线性、多项式、幂函数、指数、对数等作为基础函数形式，建立材料参数、力学参数、结构参数等与挤压强度的非线性关系，辨识出具有最优精度的映射关系作为非线性辨识约束；然后，将物理规律约束与非线性辨识约束以损失函数的形式嵌入神经网络中指导模型训练。在案例验证中开展X850材料两种铺层的单钉连接挤压强度预测，分析结果表明，两种铺层挤压强度的平均相对误差MRE分别为1.24%、1.27%。MCI-PINN在离散程度预测、可解释性、泛化能力等方面，与ANN、PINN相比表现出优异性。

Although deep learning based black-box models demonstrate high efficiency and accuracy in establishing input-output mappings for composite bolted joint strength prediction， their inherent lack of physical interpretability obscures model decision logic， ultimately compromising reliability and generalizability. Based on the fusion of physical law constraints of composite materials and nonlinear identification constraints， a Multi-Constraint Identification Physics-Informed Neural Network （MCI-PINN） is proposed. Firstly， the constraints of physical laws apply the engineering estimation formula for the extrusion strength of composite material bolt connections. Secondly， using linear， polynomial， power， exponential， and logarithmic functions as basic functional forms， nonlinear relationships between material parameters， mechanical parameters， structural parameters， and extrusion strength are established， and the mapping relationship with the highest accuracy is identified to serve as a constraint for nonlinear identification. Then， the physical law constraints and nonlinear identification constraints are embedded in the neural network in the form of loss functions to guide the model training. Finally， in the case verification， the single-pin connection extrusion strength prediction of two layers of X850 material was carried out. The analysis results show that the prediction error index MRE of the extrusion strength of the two layers is 1.24% and 1.27% respectively. In terms of discreteness prediction， interpretability and generalization ability， MCI-PINN shows superiority compared with ANN and PINN.