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Fine-grained aspect-based sentiment analysis involves aspect term extraction and aspect sentiment classi-fication. Most existing research methods address them in an independent fashion, which lack a mechanism to account for the relevant information between each other, resulting in training redundancy and waste of resources. To solve the above problems, a joint model based on position embedding and graph convolutional network (PE-GCN) under the framework of multi-task learning is proposed, which is an end-to-end approach to the overall solution of aspect term extraction and aspect sentiment classification. Firstly, the model learns the semantic feature representation of sentence through a bidirectional gated recurrent unit network. Then, it exploits positional embedding to enhance the recognition of aspect terms in sentence, and uses the graph convolutional network to generate a contextual representation containing syntactic information. Finally, interactive attention network is used to model the semantic relationship between context and aspect terms, and the sentiment polarity of aspect terms is output through softmax. Experimental results on the SemEval-2014 public datasets show that the performance of the proposed model has a significant improvement compared with other existing models.