By quantitatively describing and analyzing the changing laws of opinions in knowledge collaboration, in order to design more scientific and reasonable knowledge collaboration rules. Considering the differences of individuals and the complexity of collaboration, the uncertainty of individual opinion selection is difficult to represent by a completely deterministic function or set of functions. The individual opinion selection probabilities are considered as a time series, and a suitable ARIMA model is built to fit and short-term expectation of individual opinion selection probabilities. By organizing an experiment to simulate the knowledge collaboration process, an eye tracker is introduced to collect information on individual eye movements in order to obtain real-time changes in the opinions held by individuals, and twelve sets of individual opinion selection probability sequences were obtained and modeled separately. The empirical results show that the ARIMA model can well simulate and predict the evolutionary trend of individual opinions. This method can effectively overcome the difficulties that the influencing factors of individual opinion changes are difficult to fully grasp and data are not easily obtained in the process of knowledge collaboration.
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