Abstract: The peer review of academic papers can directly reflect the subjective evaluation of reviewers on the papers,and the extraction of sentiment information from the review text is beneficial to mining rich information of reviewers’ evaluation on each dimension of the papers.The existing sentiment analysis task could only extract the single review dimension and sentiment of experts.A fine-grained sentiment analysis model for peer review is proposed based on multi-task learning.The model is equipped with the ability to accomplish both attribute word extraction and fine-grained sentiment analysis tasks by adding the BiLSTM-CRF module to the BERT-LCF model in a multi-task learning framework.Compared with the traditional single-task fine-grained sentiment analysis model based on the Pipeline model,the proposed model can complete the review attribute extraction and sentiment analysis tasks simultaneously while ensuring the accuracy of the model.In the two tasks,F1-score of the proposed model reaches 89.01% and 90.71%,respectively.In addition,the introduction of BiLSTM-CRF module has a certain enhancement effect on the review text attribute word extraction task in a multi-task scenario,as demonstrated by comparison experiments.