In cultural heritage field, entity nesting of cultural relics data is obvious, the entity boundary is not unique, and the marked data in the field of cultural relics is extremely lacking. All the problems above can lead to the low recognition performance of named entities in the field of cultural relics. To address these issues, we construct a dataset called FewRlicsData for NER in the field of cultural heritage and propose a knowledge-enhanced, low-resource NER method RelicsNER. This method integrates the semantic knowledge of category description information into the cultural relics text, employs the span-based method to decode and solve the entity nesting problem, and uses the boundary smoothing method to alleviate the overconfidence problem of span recognition model. Compared with the baseline model, the proposed method achieves higher F1 scores on the FewRlicsData dataset and demonstrates good performance in named entity recognition tasks in the cultural heritage field. Experimental results on the public dataset OntoNotes 4.0 indicate that the proposed method has good generalization ability. Additionally, small-scale data experiments on OntoNotes 4.0 and MSRA datasets show that the performance of the proposed method surpasses that of the baseline model, demonstrating its applicability in low-resource scenarios.