Aiming at the importance of the analysis for public opinion on Internet,the authors propose a high-performance ex-traction method for public opinion. In this method,the space model for classification is adopted to describe the relationship between words and categories. The combined feature selection method is used to remove noisy words from the original feature space effectively. Then the category weight of words is calculated by the improved formula combining the frequency of words and distribution of words. Finally,the class weights of the not-catego-rized documents based on the category weight of words are ob-tained for realizing opinion extraction. Experiment results show that the method has comparatively high classification and good stability. Aiming at the importance of the analysis for public opinion on Internet, the authors propose a high-performance ex-traction method for public opinion. In this method, the space model for classification is adopted to describe the relationship between words and categories. The combined feature selection method is used to remove noisy words from the original feature space effectively. Then the category weight of words is calculated by the improved formula combining the frequency of words and distribution of words. Finally, the class weights of the not-catego- rized documents based on the category weight of words are ob-tained for enabling opinion extraction. Experiment results show that the method has comparatively high classification and good stability.