Abstract: The traditional rumor recognition method consumes manpower and material resources and has a low accuracy rate. In order to effectively identify rumors in social networks, a rumor recognition method based on a fusion model is proposed.Firstly, the text sentence vector is constructed through the BERT pre-training model. Secondly, the TextCNN model is constructed to mine the semantic features of the text, and the TextRNN model is constructed to mine the temporal features of the text. Finally, the two models are weighted and fused to realize the identification of rumors. In addition, the original mainstream model has been improved, One is to learn from the idea of the Inception model to increase the depth of the TextCNN model, and the other is to inject the attention mechanism into the TextRNN model to increase its interpretability and generalization capabilities. The experimental results show that compared with the current mainstream rumor recognition method, the accuracy of this method can reach 97.12% and the F1 value can reach 97.14%.