For the problem of early fatigue damage detection and damage degree assessment in aluminum plates, this paper proposes a damage quantitative assessment method based on anomaly index ( AI). In view of the nonlinear nonstationary and chaotic dynamic characteristics of the structural system response caused by fatigue damage of aluminum plate, the signal time-frequency transformation and phase space reconstruction method are introduced to extract multidimensional damage features of aluminum plate, and the damage sensitive features are selected according to the monotonicity and the correlation between the features and damage degree. The aluminum plate damage detection problem is converted into a binary classification problem with a set of damage-sensitive features in the state description space, and a self-organizing feature mapping (SOM) network is used to identify the health status of aluminum plate. In order to further quantitatively characterize the damage degree of the aluminum plate, the SOM is used to fuse the damage sensitive features, and the AI values are used to quantitatively evaluate the damage state of the aluminum plate. The results of simulations and experiments showed that the SOM-based anomaly index proposed in this paper has high sensitivity and good dynamic tracking capability for fatigue damage evolution of aluminum plates, and has both good application prospects in the health monitoring and management of aluminum plate structures.