Abstract: The stock market occupies an important position in the capital market and is a barometer of the economy.Experts' comments on stocks are an important basis for investors to make investment decisions.Therefore,how to quickly and effectively capture the subject information of many expert stock reviews has become a hot spot in the field of stock research.However,most stock text topic recognition algorithms currently use a single standard for their feature selection methods and classification models.In general,a single standard can only reflect the recognition of a text topic from one side,and cannot fully capture the subject's main features.In fact,different feature selection criteria and classifier models understand the text from different sides,and the captured feature information has strong complementarity.To this end,in order to improve the accuracy of the theme recognition of stock texts,this paper has a multi-faceted fusion of stock texts from the perspective of information fusion,it includes:1)Feature selection layer,which performs weighted fusion on multiple feature selection methods to enable it to fully characterize stock text features;2)The decision-making layer,based on SVM-score,performs decision-making layer fusion on multiple classifiers,which can improve the accuracy of text recognition.Experiments based on measured data show that the recognition accuracy of the multi-layer fusion algorithm proposed in this paper is significantly improved compared with the single-mode text topic recognition method.

Key words: Feature fusion, Feature selection, Subject recognition, SVM-score, Text categorization