Current analytical method based on short-text can not describe the evolution process of burst-event in a simple and accurate manner. In order to solve the problem, a new method was proposed to express the evolution process of burst-event based on short-text data sets. Firstly, a method of measuring event status was proposed to describe the state of event at each time for analyzing the development process of the event. Secondly, according to the structured information of short-text, the value of event status was set from two aspects: text information and user information. Thirdly, with the consideration of the impact factor of text information, the weight of text information was calculated by constructing related formulas. Fourthly, with the consideration of the impact factor of user information, a modified PageRank algorithm was proposed, and users were divided into different layers to calculate the weight of user information by constructing related formulas. Finally, the weight of text information and the weight of user information were combined to calculate the value of event status. The experimental results show that considering user information in turn, the modified PageRank algorithm, and the idea of dividing the users into different layers all can correct 1~2 points of description and improve the accuracy of expressing the evolution process of event.

Because of the literature search system failing to comprehend users' real-time demands, a method to find users' real-time demands for literature search systems was proposed. Firstly, this method analyzed the users' personalized search behaviors such as browsing and downloading. Secondly, it established users' real-time Requirement Documents (RD) based on the relations between users' search behaviors and users' requirements. And then it extracted keyword network from requirement documents. Finally, it gained users' demand graphs which were formed by core nodes extracted from keyword network by means of random walk. The experimental results show that the method by extracting demand graphs increases the F-measure by 2.5%, in the comparison of the K-medoids algorithm on average, under the condition that users' demands are emulated in the experiment. And it also increases the F-measure by 5.3%, in the comparison with the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm on average, under the condition that users really searches for papers. So, when the method is used in literature search systems where users' requirements are stable, it will be able to gain users' demands to enhance users' search experiences.