Sentiment analysis of massive text information on social networks can better mine the behavior rules of Internet users,helping decision-making institutions understand the public opinion tendencies and helping businesses improve the quality of service. The task of Chinese implicit sentiment classification is more difficult than those of other languages due to the absence of key emotional features,expression vector forms and cultural customs. The existing Chinese implicit sentiment classification methods are mainly based on Convolutional Neural Network(CNN),and have some defects, such as the inability to obtain the sequence of words and not using contextual emotional features reasonably in implicit emotion discrimination. A Chinese implicit sentiment classification model combining sequence and contextual features named GGBA (GCNN-GRU-BiGRU-Attention) was proposed to solve the above problems. In the model, Gated Convolutional Neural Network (GCNN) was used to extract the local important information of sentences with implicit sentiments,and Gated Recurrent Unit(GRU)network was used to enhance the temporal information of features. In the context feature processing of sentences with implicit sentiments,the combination of Bidirectional Gated Recurrent Unit (BiGRU)and attention was used to extract the important emotional features. After obtaining the two types of features,the contextual important features were integrated into the implicit emotion discrimination through the fusion layer. Experimental results on the implicit sentiment analysis evaluation dataset showed that the macro average precision of GGBA model was 3. 72% higher than that of normal text CNN named TextCNN,2. 57% higher than that of GRU,and 1. 90% higher than that of Disconnected Recurrent Neural Network(DRNN). Therefore,GGBA model achieves better classification performance than the basic models in implicit sentiment analysis tasks.