A novel individual-group-merchant relation model is proposed to automatically identify fake reviews on E-commerce platforms, which focuses on the characteristics of fake reviewers’ behaviors instead of review contents. Three sets of indicators are proposed, i.e. individual indicators, group indicators and merchants’ indicators. To validate the model, an empirical study of fake review identification from a Chinese E-commerce platform is implemented. A number of 97804 reviews posted from 9558 different IP addresses, which are related to 93 online stores, are selected as test data. Results show that the F1-measure values of the proposed model on identifying fake reviewers, online merchants and groups with credit manipulation are 82.62%, 59.26% and 95.12%, respectively. Utilizing logistic regression and K nearest neighbor classifier based on the comments of the content as the baseline methods, the F1-measure values are 52.63% and 76.75%, respectively. Thus, the IGMRM model outperforms traditional methods in identifying fake reviewers.