For insufficient edge weight window threshold design in Text Graph Convolutional Network （Text GCN）， to mine the word association structure more accurately and improve prediction accuracy， a fake review detection algorithm combining Gaussian Mixture Model （GMM） and Text GCN named F-Text GCN was proposed. The edge signal strength of fake reviews that are relatively weak compared to normal reviews in training data size was improved by using GMM nature to separate noise edge weight distributions. Additionally， considering the diversity of information sources， the adjacency matrix was constructed by combing documents， words， reviews and non-text features. Finally， the fake review association structure of the adjacency matrix was extracted through spectral decomposition of Text GCN. Validation experiments were performed on 126 086 actual Chinese reviews collected by a large domestic e-commerce platform. Experimental results show that， for detecting fake reviews， the F1 value of F-Text GCN is 82.92%， outperforming BERT （Bidirectional Encoder Representation from Transformers） and Text CNN by 10.46% and 11.60%， respectively， the F1 of F-Text GCN is 2.94% higher than that of Text GCN. For highly imitated fake reviews which are challenging to detect， F-Text GCN achieves the overall prediction accuracy of 94.71% by secondary detection on the samples that Support Vector Machine （SVM） was difficult to detect， which is 2.91% and 14.54% higher than those of Text GCN and SVM. Based on study findings， lexical interference in consumer decision-making is evident in fake reviews’ second-order graph neighbor structure. This result indicates that the proposed algorithm is especially suitable for extracting long-range word collocation structures and global sentence feature pattern variations for fake reviews detection.