This work addresses the challenge of identifying the provenance of illicit cultural artifacts, a task often hindered by the lack of specialized expertise among law enforcement and customs officials. To facilitate immediate assessments, we propose an improved deep learning model based on a pre-trained ResNet model, fine-tuned for archaeological artifact recognition through transfer learning. Our model uniquely integrates multi-level feature extraction, capturing both textural and structural features of artifacts, and incorporates self-attention mechanisms to enhance contextual understanding. In addition, we developed two different artifact datasets: a dataset with mixed types of earthenware and a dataset for coins. Both datasets are categorized according to the age and region of artifacts. Evaluations of the proposed model on these datasets demonstrate improved recognition accuracy thanks to the enhanced feature representation.