User profile modeling method is the key bott information to progress. Existing algorithms and sy drawbacks of inaccurate spatial location and biased eneck that restricts theory of intelligent distr stems of intelligent distribution of spatia utility, etc. and are mostly concerned on bution of spatial nformation have the contribution of the user＇s retrieval behavior to the profile model, but not consider time factors at all, and pay littte attention to the role of user feedback. In view of this, the theories and algorithms of the existing literature are extended, by introducing concepts and arithmetic of region number, interest degree, interest degree density, etc. , and dynamic factors of weight attenuation function and user information feedback, etc., to make a model able to adjust more accurately and in time with the change of user preference profile. The experimental results show that, compared with traditional static models, the model can more effectively reflect the change of user preference profile.%用户偏好模型的构建方法是制约空间信息智能分发理论取得进展的关键瓶颈。现有的空间信息智能分发算法和系统存在空间范围定位不准确、效用度计算存在偏差等缺陷，且大多关注用户的检索行为对偏好模型的贡献却均未考虑时间因素的影响，也很少注意到用户反馈的作用。鉴于此，对现有文献的理论和算法进行扩展，通过引入区域数、兴趣废、兴趣度密度等概念和算法，以及权值衰减函数和用户信息反馈等动态化因素，使模型能够更为准确、及时地随着用户偏好特征的变化进行修正。试验表明，相较于传统的静态模型而言，该模型能够更为有效地反映用户偏好特征的变化。
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