The analysis and study of readers' emotion is helpful to find negative information of the Internet, and it is an important part of public opinion monitoring. Taking into account the main factors that lead to the different emotions of readers is the semantic content of the text, how to extract semantic features of the text has become an important issue. To solve this problem, the initial features related to the semantic content of the text was expressed by word2vec model. On the basis of that, representative semantic word clusters were established for all emotion categories. Furthermore, a strategy was adopted to select the representative word clusters that are helpful for emotion classification, thus the traditional text word vector was transformed to the vector on semantic word clusters. Finally, the multi-label classification was implemented for the emotion label learning and classification. Experimental results demonstrate that the proposed method achieves better accuracy and stability compared with state-of-the-art methods.