中文摘要：

在位置服务领域，用户所处环境的上下文信息在分析、处理请求，以及推送相应的位置信息服务方面发挥着至关重要的作用。目前，如何存储和管理上下文位置信息缺乏统一的模型和标准，本文对此提出了一种全新的位置服务的上下文信息模型。利用User Context（＆lt;User＆gt;，＆lt;Time＆gt;，＆lt;Location＆gt;，＆lt;Surroundings＆gt;，＆lt;Demand＆gt;）5元素模型描述位置服务上下文信息中5个信息元素（用户信息、时间信息、位置信息、环境信息、用户需求信息），这5个信息元素均是直接因素，彼此独立且获取方便，人为干预少；同时，利用数据库技术可将5元素模型抽象成5元素表形式存储于数据表中，以便高效检索。最后，通过分析5元素模型中的不同信息元素，可推理出基于搜索关键词的用户需求偏好及基于时间和位置信息的用户轨迹（用户行为、热点区域、用户兴趣）。

英文摘要：

In the area of location service, the location information of user based on context-aware play an impor-tant role in analyzing, processing and sending location information service. That is, if using the context about the environment around the user well, we could analyze the request of the user better and provide the most appreci-ate service to the users in time which could indeed meet the users’request. However, how to store and manage context information, we have not a uniform model and standard yet. Moreover, there is no specific model de-signed for the location context information of user. This paper applies the 5-ary model which is designed to ex-press context information to propose a new method specifically for the location information based on con-text-aware of the user. The model is：User Context （＆lt;User＆gt;,＆lt;Time＆gt;,＆lt;Location＆gt;,＆lt;Surroundings＆gt;,＆lt;Demand＆gt;）. This model could state the five key information elements, that is user information, location information, time, surroundings information and user demand information. These five elements are direct and independent. More-over, they could be acquired easily. The user information may include name, sex, job, major of the user and so on. The location is made up with two parts：textual address and coordinate. The coordinate information is usually gathered by GPS. The surrounding information is about weather and temperature which could be received from the relevant website. And the demand information is text which is used to describe the user’s demand or request, mostly about restaurant, shopping, entertainment and so on. Then, store the users’location context information into a database in the way of the 5-ary to improve the query speed of data. In the end, through extracting the key-words in the demand information with TF-IDF method, we can conclude the inclination of users. Based on time and location information, we could also acquire some initial conclusions including the trend of demand informa-tion from