The invention provides a text sentiment analysis method based on deep learning. The method comprises the following steps: (1) inputting text data, removing stop words, and extracting keywords to forma keyword set; (2) forming a dense sub-graph by constructing a keyword co-occurrence graph; vector representations of sentences in the sub-graphs and the document are obtained, and then the sentencesare distributed to the sub-graphs; designing edge connection and edge weight between the sub-graphs to form topological interaction graph expression of the document; and (3) taking the topological interaction diagram as the input of an Emo-GCN model, carrying out node feature extraction transformation, and then fusing local structure information to obtain a node aggregation matrix. The nonlinear transformation is carried out on the aggregated information. The Emo-GCN model adopts a hierarchical structure, and the features are extracted layer by layer. According to the method, the novel topological interaction graph is adopted to express the text information, then the graph convolutional neural network is used for text sentiment analysis, and the method still has strong adaptability. The method is applied to product recommendation, market prediction and decision adjustment, and has extremely high commercial value.