Correctly identifying the entities in judgment documents is an important foundation for building legal knowledge graph and realizing smart courts. However， commonly used Named Entity Recognition （NER） models cannot solve the problem of polysemous word representation and entity boundary recognition errors in judgment document well. In order to effectively improve the recognition effect of various entities in the judgment documents， a Bidirectional Long Short-Term Memory with a sequential Conditional Random Field （BiLSTM-CRF） based on Joint Learning and BERT （Bidirectional Encoder Representation from Transformers） （JLB-BiLSTM-CRF） model was proposed. Firstly， the input character sequence was encoded by BERT to enhance the representation ability of word vectors. Then， the long text information was modeled by BiLSTM network， and the NER tasks and Chinese Word Segmentation （CWS） tasks were jointly trained to improve the boundary recognition rate of entities. Experimental results show that this model has the precision of 94.36%， the recall of 94.94%， and the F1 score of 94.65% on the test set， which are 1.05 percentage points， 0.48 percentage points and 0.77 percentage points higher than those of BERT-BiLSTM-CRF model respectively， verifying the effectiveness of JLB-BiLSTM-CRF model in NER tasks for judgment documents.