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The fine-grained sentiment analysis is one of the key problems in the area of natural language processing.By learning contextual information of the text to conduct sentiment analysis on specific aspects,it can help users and businesses to better understand the sentiment information of specific aspects of users' comments.Aiming at the task of fine-grained sentiment analysis on users' comments,a text sentiment classification model combining BiGRU-attention and Gated Mechanisms is proposed.By integrating existing sentiment resources,HOWNET evaluation sentiment dictionary is used as the seed sentiment dictionary to expand the user comment sentiment dictionary through SO-PMI algorithm,the negative dictionary and part of speech information are combined to expand the user comment sentiment knowledge as the users' comment sentiment characteristic information.Introducing word,character and sentiment characteristics as the model of input infotmation,and using BiGRU to extract deep text features,then combined with gated mechanism as well as the attention mechanism,according to the acquired aspect word information to further extract the contextual sentiment characteristics related to aspect words,the final sentiment polarity is obtained by the softmax classfier.Experimental results show that the proposed model achieves better experimental results on the AIchallenger 2018 fine-grained sentiment analysis Chinese data sets,the Macro_F1_ score value reaches 0.7218,and the performance exceeds the baseline system.