With the development of the mobile Internet, Microblog topic has become popular. A single hot topic may have tens of thousands of comments. The stance detection of Microblog topic aims to automatically determine whether the author of a text is in favor of the given target, against the given target, or neither. Firstly, Word2Vec trains out each word of the corpus of vector to extract semantics information from sentence. Then, TextRank keywords extracted method is used to construct the thematic words set as the stance's feature, meanwhile, the sentiment lexicon is used to extract the sentiment information of the sentence. Finally, the word vector of feature selection is trained and predicted by Support Vector Machine (SVM), so as to complete the model of stance detection. The experimental result shows that the stance feature based on the combination of thematic words and sentiment words can obtain good stance detection effect.