The damage to aluminized 321 steel, which is the main material of solar thermal power heat exchange tube, will lead to the shortening or even fracture of the life of the heat exchange tube. Therefore, the damage detection must be carried out. The damage characteristics of aluminized 321 steel are analyzed by the acoustic emission (AE) method, and the online dynamic monitoring of heat exchange tube performance is realized. The damage degree of aluminized 321 steel is characterized by using the AE Ib-value feature, and the self-organized mapping (SOM) neural network algorithm is used to cluster the AE characteristic parameters to analyze the damage mode of the material. The results show that the number of AE events in the mechanical plastic stage increases sharply, and the peak values of energy and ringing count indicate the fracture of the specimen. In addition, before the failure of the specimen, the Ib-value is significantly reduced and the density becomes dense, indicating that the variation characteristics of the Ib-value can be used as an early warning signal for the critical failure of the material. Four clusters and their corresponding characteristic frequencies are obtained by clustering analysis of the characteristic parameters through the SOM algorithm. The fracture morphology of the specimen is observed by scanning electron microscope ( SEM). The four clusters correspond to four types of damage modes, including hole growth and coalescence, micro-crack nucleation, macro-crack propagation, and fibrous fracture. This study aims to explore the damage evolution behavior of metal pipes and provide a basis for damage analysis and health monitoring of pipes.