Understanding the interests and behaviors of Web users serves as the core of many Web usage data mining applications such as personalized search, recommendation, personalization, business decision, advertisement targeting, marketing and customer relationship management. In this paper, an approach of building an adaptive user profile based on Memory-Model is proposed. The adaptive user profile gives a quantitative measurement of user interests and their changes, which reflects and predicts the interests dynamically and quantitatively, such as the enhancing, decaying of interests and the new interests coming out. It can differentiate long-term and short-term interests as well. It is applied to rank the topic list in which users are interested. In 17160 cases of user log, 70.94% of the nDCG results between user interest ranking list based on memory model and actual ranking list of user are above 90%, significantly higher than reference modeling's.