Micro-blog has become an important platform for information sharing. Meanwhile, it is also one of the main ways for spreading of different misinformation. In order to detect the micro-blog misinformation quickly and effectively, a method based on Gradient Boost Decision Tree (GBDT) was proposed. Firstly, classification features of content, user properties, information dissemination and time characteristic were extracted from the comments of micro-blog. Then an identification model based on GBDT algorithm was proposed to detect misinformation. Finally, two real micro-blog datasets were used to verify the efficiency and effectiveness of the model. The experimental results show that the proposed model can effectively improve the accuracy of micro-blog misinformation detection.