Abstract: When constructing a knowledge graph in the field of party building， the traditional named entity recognition （NER） methods often suffer from unclear entity boundaries and polysemy of entity terms， which lead to low recognition accuracy and efficiency. To address these issues， this paper proposes a BERT-BiLSTM-CRF entity recognition model that integrates tree-like probability and a domain dictionary. The model involves embedding the domain dictionary into BERT for text vectorization， utilizes BiLSTM to acquire contextual semantic features， and applies tree-like probability to the transition probability calculation in the CRF layer to enhance word segmentation accuracy. The experimental results on the MSRA and self-constructed corpora， compared with the baseline model， show that the proposed model achieves better performance in terms of F1-score， recall， and precision.

Key words: BERT-BiLSTM-CRF model, tree-like probability, domain dictionary, name entity recognition