Abstract: The structural health of subway shield tunnels is directly related to the safety of urban operations. In this paper, we systematically analyze the multi-dimensional interactive causes of water seepage, cracks, misalignments, etc., and review the limitations of mainstream inspection techniques in depth: fiber optic sensing is expensive and complicated to install; 3D laser scanning is difficult to detect internal defects; infrared thermography is insufficient to identify the deep-seated defects; geo-radar (GPR) interpretation is highly dependent on experience; methods based on dynamical properties (VMD) are susceptible to interference from ambient noise; deep learning requires massive labeled data and has limited generalization capability. The core bottleneck is that a single technology cannot meet the demand for accurate sensing of the whole life cycle and multi-dimensional diseases. The breakthrough direction requires the construction of a multi-source heterogeneous data fusion framework-integrating apparent scanning (laser/infrared), internal detection, distributed response (fiber optic), overall dynamic characteristics (vibration) and environmental parameters, and eliminating information silos through a unified spatial and temporal reference. Synchronized development of intelligent decision-making models coupled with physical mechanisms and data-driven (fusion of digital twins and Bayesian updating), realizing a three-level leap from passive detection to active warning to condition assessment to optimized maintenance decision-making. The collaborative innovation of multi-source perception and intelligent decision-making is the fundamental path to overcome the problem of hidden disease diagnosis and realize the double optimization of safety and operation and maintenance efficiency.