Bronze inscriptions are invaluable for studying ancient politics, economy, and culture. However, minimal stylistic variations and the predominance of unlabeled data in unearthed inscriptions pose challenges for computer-aided inscription analysis. To address this issue, a bronze inscription age clustering network based on a deep unsupervised clustering model is proposed. In the first stage, a ResNet50-based feature extraction module is constructed, incorporating an improved multiscale CBAM attention mechanism. This enhancement allows the network to simultaneously capture detailed and global features, thereby overcoming the limitations of traditional feature extraction methods that struggle with incomplete feature representation for inscriptions of similar ages. In the second stage, K-means clustering is applied to the extracted features. The clustering branch results serve as pseudo-labels, which are then used to compute the cross-entropy loss against the predictions of the model's prediction branch. In the third stage, iterative training is performed using cross-entropy loss backpropagation to continuously optimize the model parameters, enhancing the accuracy of feature extraction and clustering. The experimental results demonstrate that the proposed network achieves an overall accuracy of 89.43% on the standard inscription dataset, surpassing traditional unsupervised clustering networks by more than 14%.