Acoustic emission (AE) signals exhibit a strong correlation with concrete damage. However, the relationship between column damage and AE signals under eccentric loading conditions, combined with the application of traditional RA-AF classification methods for crack characterization, demonstrates limitations. These approaches provide insufficient resolution to accurately identify damage types throughout the entire structural failure process. This study employed K-means clustering algorithm and Gaussian mixture models (GMMs) to analyze AE signal features from reinforced concrete (RC) columns undergoing failure under the eccentric compression loading of different eccentricity. Subsequently, a random forest model was used for automated damage stage classification. Experimental results demonstrate that the damage progression in eccentrically compressed columns comprises four distinct stages, each exhibiting unique AE signal characteristics. The integrated approach of clustering and random forest modeling demonstrates robust feasibility in identifying AE signal patterns associated with specific damage stages, achieving an 85% recognition rate for damage stage classification. These findings provide quantitatively validated evidence supporting the efficacy of machine learning-based methodologies for enabling stage-specific damage characterization in structural health monitoring applications.