Online reviews play a significant role in e-commerce. Consumer has been more relied on them when making decision in purchasing. However, unethical businesses may spread deceptive reviews to manipulate consumer`s opinion. Research by Ott et al. (2011) [2] showed that humans can only identify fraud reviews with only an accuracy of 57.3%. Besides, recent research face a crucial challenge that the cross-domain classification model is too rely on similar datasets from the same domain, which causes in a sharp decline in accuracy when testing on datasets from different domain. Currently, there is a lack of method on text features or rules to share with different domains Hence, our study proposes a model based on Bidirectional Encoder Representations from Transformers (BERT). We suggest replacing domain-specific words in reviews with [MASK] to overcome the significant stylistic differences between cross-domain reviews. Our research utilizes reviews from Ott et al. (2011) [2] and Li et al. (2014) [3] in the domains of restaurants, hotels, and doctors, supplemented with Yelp reviews as real data for training. Finally, we compare the results of MASK-BERT with the state-of-the-art approach by Ren & Ji (2017) [4]. In the cross-domain, particularly in the doctor domain with larger content differences, our proposed masking mechanism leads to a highest accuracy improvement of 15–20%.