Since there are many troubles such as large quantity, uneven quality and poor credibility in getting helpful information and making effective decision for stakeholders, and the existing research work on credibility assessment mainly considers the sources of reviews and the support of reviews in form of votes, an assessment method on review credibility from perspective of intrinsic quality was proposed. That is, the credibility assessment of reviews was realized by integrating the ratings of reviewers, the support degree of reviews and the consistency in reviews, etc. Firstly, the pre-processing of review data was completed based on rule and method libraries. Then, the feature recognition and the feature value extraction and standardization were completed based on product feature library, generic dictionary, sentiment dictionary and method library. Finally, the credibility assessment of reviews was completed based on the established models. The experimental results verify the feasibility of this method, and it can be applied to assess the credibility of product reviews automatically on other e-commerce platforms.

Since information extraction algorithm for Web pages lacks generality and information of title, release-time and source in news Web page, a new information extraction algorithm was proposed to resolve those problems. Firstly, HTML code of Web page was parsed to text sets combined with line number and text; then, extractor began to search boundary of news content from line which the longest sentence belonged to due to the characteristic that the longest sentence belongs to the content of news with an extremely high probability. Meanwhile, the longest common string algorithm was used to extract title, the regular expression and line number were used to extract release-time, and the presentation characteristics of source and line number were used to extract source. Finally, a data set was built to conduct a comparison experiment with an open-source software named newsPaper in accuracy of extraction. Experimental results show that newsExtractor outperforms newsPaper in average accuracy of content, title, release-time and source, it has strong generality and robustness.

Traditional solutions deal with following-car interference and adjacent-lane interference in Electronic Toll Collection (ETC) system separately, and are of low efficiency and high cost. In order to solve this problem, a solution based on information fusion, which can deal with these two problems together, was proposed. This method used the vehicle information collected by an ETC system as known information and chose the features of the vehicle image to verify whether the vehicle information came from the car in question. Then D-S evidence theory was adopted to fuse the results of verification and make a final decision whether it was the right car to be charged. An improved method for D-S evidence theory was proposed to ensure its validity when the results of consistency verification conflicted with each other. The experimental results show that this method can highly reliably detect illegal vehicles and solve both following-car interference and adjacent lane interference problems in ETC system.