Ancient Chinese texts are rich in historical and cultural information. Studying entity relationship extraction of such texts and constructing related knowledge graphs play an important role in cultural inheritance. Given the large number of rare Chinese characters, semantic fuzziness, and ambiguity in ancient Chinese texts, the entity relation joint extraction model based on the BERT-ancient-Chinese pre-trained model (JEBAC) is proposed. First of all, BERT-ancient-Chinese pre-trained model integrates the BiLSTM neural network and attention mechanism (BACBA), identifies all subject and object entities in sentences, and provides a basis for joint extraction of relation and object entities. Next, the normalized coding vector of the subject entity is added to the embedding vector of the whole sentence to better understand the semantic features of the subject entity in the sentence. Finally, combined with the sentence vector with the characteristics of the subject entity and the prompt information of the object entity, the relationship and object entity in the sentence are jointly extracted by BACBA to obtain all triple information (subject entity, relationship, and object entity) in the sentence. The performance of Chinese entity relation extraction DuIE2.0 datasets and the classical Chinese entity relation extraction C-CLUE small sample datasets of CCKS 2021 are compared with that of the existing methods. Experimental results show that the proposed method is more effective in extraction performance, with F1 values up to 79.2% and 55.5%, respectively.