Truth discovery is one of the challenging research hotspots in the field of data integration. Traditional methods use the interaction between data sources and values to infer the truth， which lack sufficient feature information. Deep learning-based methods can effectively perform feature extraction， but their performance depends on a large number of manual annotations， and it is difficult to obtain a large number of high-quality truth labels in practical applications. To overcome these problems， this paper proposes an unsupervised truth discovery method based on multi-feature fusion（MFUTD）. First， ensemble learning is used to label truth without supervision. Then， the pre-training Bert model and the one-hot coding method are used to obtain the semantic features and interactive features of the values. Finally， the initial training set is constructed by fusing multiple features of the values and using their “truth” labels to train the truth prediction model by self-training. Experimental results on two real data sets show that the proposed method has the higher truth discovery accuracy than the existing methods.