Identifying the age of ancient bronze vessels requires many relevant historical materials, takes a long time, and has strong subjectivity. We propose a new approach to assist archaeologists in analyzing and dating ancient bronze artifacts. The proposed method applies deep learning methods for age discrimination of ancient bronze artifacts based on image classification pre training weights. First, through multiple basic experiments, EfficientNetV2-L with good discrimination results is selected as the baseline model from four representative network models. Thereafter, EfficientNetV2-L is used to extract features from the ancient bronze ware dataset, and then, the original linear classification layer is replaced with cosin_classifier to reduce the risk caused by variance and improve the model's discrimination ability. Finally, the focal loss function is introduced to replace the original cross entropy loss function for loss calculation. Under the influence of the focusing parameter and class weighting factor, the poor model learning performance caused by a small number of samples and categories is effectively reduced. The proposed method improves the accuracy, precision, recall, F1 score, and area under the curve by 4.1 percentage points, 4.0 percentage points, 4.1 percentage points, 4.2 percentage points, and 0.9 percentage points, respectively, compared to the original EfficientNetV2-L, achieving an optimal accuracy of 91.7% on the test set. Additionally, a model prediction analysis is conducted on controversial bronze artifacts with different stages. The results indicate that deep learning technology is effective in identifying the age of ancient bronze ware datasets, providing reference analysis data and reducing the workload of archaeological experts.