Fiber Bragg grating (FBG) sensors are widely used in the structural health monitoring due to the high sensitivity, electromagnetic interference resistance, and multiplexing capabilities. However, the FBG sensors are prone to fatigue degradation under the cyclic loading. Traditional diagnostic methods often rely on manual feature extraction or physical modeling, making it difficult to effectively capture the subtle damage signals at the early-stage. Thus this paper proposes an end-to-end spectral intelligence monitoring model named CABiL in order to solve the early diagnosis problem of FBG sensors during the fatigue degradation. The key contribution of CABiL lies in its deep integration of convolutional neural networks (CNN), multi-head attention (MHA) mechanism, and bidirectional long short-term memory networks (BiLSTM), which forms an automatic feature extraction and time-series modeling framework. The model employs a 1D-CNN to automatically extract local morphological features from spectral data by eliminating the need for manual feature selection. MHA enhances the model′s sensitivity to early spectral changes caused by subtle damage, allowing it to focus on key regions of spectrum where fatigue-induced distortions occur. BiLSTM effectively captures the temporal evolution patterns of spectral data during the loading process, integrates global dependencies and dynamic information, thus improving the modelling ability of complex damage processes. This end-to-end learning framework does not require complex physical modeling, offering the high inference efficiency. Experimental results show that CABiL achieves the state classification accuracy of over 95% for FBG, which provides the F1 scores above 0.93 for all categories. The proposed spectral intelligence monitoring method provides a highly reliable and real-time intelligent diagnostic solution for the health management of FBG sensors, which also advances the structural health monitoring towards smarter, lightweight systems.