The flood of fake job advertisements will not only damage the legitimate rights and interests of job seekers but also disrupt the normal employment order， which results in a poor user experience for job seekers. To effectively detect fake job advertisements， an SSC （Semi-Supervised fake job advertisements detection model based on Consistency training） was proposed. Firstly， the consistency regularization term was applied on all the data to improve the performance of the model. Then， supervised loss and unsupervised loss were integrated through joint training to obtain the semi-supervised loss. Finally， the semi-supervised loss was used to optimize the model. Experimental results on two real datasets EMSCAD （EMployment SCam Aegean Dataset） and IMDB （Internet Movie DataBase） show that SSC achieves the best detection performance when the labeled data are only 20， and the accuracy is increased by 2.2 and 2.8 percentage points compared with the existing advanced semi-supervised learning model UDA （Unsupervised Data Augmentation）， and is increased by 3.4 and 11.7 percentage points compared with the deep learning model BERT （Bidirectional Encoder Representations from Transformers）. At the same time， SSC has good scalability.