[Objective] This paper proposes a feature system and new model to improve the efficiency of early recognition, aiming to address the issues of time delay and overload in recognizing valuable content from virtual communities. [Methods] We constructed a dual-link fusion algorithm with the text semantics of user-generated content and the network structure of explicit and implicit interaction between users and texts. In the text semantic link, we used the BERT+BiLSTM+Linear to obtain the deep semantic features. In the association network link, we adopted GAT to process the shallow numerical characteristics and association characteristics of the nodes. Finally, we utilized the convolution layer to optimize the fusion information of the above dual links and achieved early value recognition. [Results] The dual-link fusion model had a processing accuracy of 89.80% for data from the Meizu Flyme community, which was 3.45% and 3.20% higher than that of the single text semantic link and associated network link, respectively. Compared with other baseline models, the accuracy and F1 values were also improved. [Limitations] The generalization ability of the model needs to be further improved, and we should have analyzed rich text content (i.e., pictures and external links). [Conclusions] The deep learning fusion model improves the accuracy of early recognition of valuable texts by processing sequential text semantics and topological network structure.