As the popularity of World Wide Web increases, many newspapers expand their services by providing news information on the Web in order to be competitive and increase benefit. The Web provides real time dissemination of financial news to investors. However, most investors find it difficult to search for the financial information of interest from the huge Web information space. Most of the commercial search engines are not user friendly and do not provide any tailor-made intelligent agents to search for relevant Web documents on behalf of users. Users have to exert a lot of effort to submit an appropriate query to obtain the information they want. Intelligent agents that learn user preferences and monitor the postings of Web information providers are desired. In this paper, we present an intelligent agent that utilizes user profiles and user feedback to search for the Chinese Web financial news articles on behalf of users. A Chinese indexing component is developed to index the continuously fetched Chinese financial news articles. User profiles capture the basic knowledge of user preferences based on the sources of news articles, the regions of the news reported, categories of industries related, the listed companies, and user specified keywords. User feedback captures the semantics of the user rated news articles. The search engine will rank the top 20 news articles that users are most interested in based on these inputs. Experiments were conducted to measure the performance of the agents based on the inputs from user profile and user feedback.