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With the rapid development of Internet applications and the rapid growth of users , the reviews and opinions of stock market largely reflect the quotation of the stock market,simultaneously it affects the ups and downs of the stock market. Therefore, how to quickly and efficiently analyze the attitudes and opinions of netizens to the stock market,which,this question plays important role in guiding us to predict the stock market. The thesis studies the rising and falling trend of stocks by analyzing the emotional polarity of different professional issuing stocks. This paper proposes a sentiment analysis method based on a dictionary of consistent integrated financial phrases and weighted at the end of paragraph, which can solve the dependency problem of sentiment dictionary on the domain，and it can effectively improve the accuracy of sentiment analysis. In addition, this paper also proposed a windowed stock prediction model, which can be used to analyze the optimal value of the forecast event window. The experimental results shows that it will be better to predict the rising or falling trend of a particular stock just based on the stock market sentiment analysis.

Using Chinese poetry as a carrier to realize text information hiding(IH) is a very hot research topic in the resent years. This kind of method has the advantages of large capacity and high concealment. However there isn′t a very effective steganalysis detection algorithm according to this kind of method. This paper puts forward a kind of poetry steganalysis detection algorithm based on convolution neural network(CNN),which can automatically judge whether the text of the input contains hidden information or not. Through training and testing the model by a large amount of data, the experiment results show that the proposed model can achieve 94.8% of detection accuracy, which shows very high poetry steganographic detection performance.

Named entity recognition is a fundamental technology in natural language processing(NLP). In recent years, rapid development of social network platforms such as microblog presents new challenges to the traditional named entity recognition(NER) technology because of the unique form. In this paper, an improved method based on the conditional random field(CRF) model is proposed for microblog texts. Due to the short texts and semantic ambiguity, external data resources are introduced to generate the topic feature and word representation feature for training the model. Due to the large-scale of microblog data and the high cost of manual standardization, an active learning algorithm based on least confidence is adopted to enhance the training effect at a lower cost of labor. Experiments on a Sina weibo data set show that this method improves the F-score by 4.54% compared to the traditional CRF methods.

In this paper, we present an experimental research on the frameless registration of DSA/CT images based on frameless localization algorithm. The result shows that, 3D fusion and registration of vessels in the DSA images and anatomical structures in CT images will help surgeons to make accurate diagnosis and on plann operative.