Abstract: Big-data analysis is a process of applying descriptive, diagnostic, predictive, and prescriptive models for data to answer specific questions or to find new insights. Taking Baidu search index as a platform, and “Sanmenxia Polytechnic” as search keywords, this paper uses Web crawler software to intercept the weekly searching number of Baidu hot words from 2012 to 2017. Through the grey prediction model, the weekly searching frequency prediction equation is obtained. Compared with the predicted value of an element linear regression model, it’s verified that the prediction equation is reasonable and effective, and the number of keywords searched in the next two-year is predicted. Finally, through data chart analysis, “Sanmenxia Polytechnic” as Baidu search keywords has significant time characteristics: firstly, the total number of search is increasing every year, secondly, the peak and valley value every week in a year has an obvious fluctuation law. Combined with Baidu search index platform to analyze the periodic search distribution of keywords, the corresponding countermeasures are put forward.

Key words: grey prediction, GM(1,1), search frequency, SPSS, big-data