In order to overcome the problems of low precision and poor recall in the current research results of user demand mining, a dynamic method based on association rule mining is proposed. Using association rules to get user behaviour-related data, analysing user behaviour through the crawler system, using different association strategies according to different businesses, combining user browsing time with a user interest attenuation factor to calculate user interest, and building a user dynamic interest model. Based on the analysis of user interest, in the initial stage of mining, support and trust are input, respectively, and an association rule mining algorithm is called to realise the dynamic mining of user implicit information demand. The experimental results show that the mining accuracy and recall rate of this method are higher than 95, and the whole method has strong scalability and practicality.
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