专利是技术知识最有效的载体，专利知识演化图谱的构建将会极大地提升专利的应用价值。项目的宗旨是：综合运用文本挖掘技术，从知识主体和知识客体两个视角，进行面向专题的专利知识演化分析。首先，对于给定的用户需求，构建相应的领域本体，用于术语识别和查询扩展，检索国家专利数据库SIPO，建立专题数据库；然后，给出专利的逻辑表示，定义专利知识的细粒度表示<Feature,Effect>、<Effect，Value>,利用命名实体识别、属性抽取、语义标注等技术，填充专利的Feature、Effect和Value值框架，将非结构化信息转化为可以量化的知识单元。在专利知识相似度和新颖度基础上进行专利的主题聚类；最后，构造基于知识主体的竞争网络和基于知识客体的共词网络，按照时间维度，研制专利知识演化图谱，进行热点专利识别、专利机会发现和专利趋势预测，建立可视化的专利挖掘平台。

Nowadays Patent is rich in the information of knowledge and plays an import role in economy. The construction of map of patent knowledge evolution arises the value of application greatly. The aim for this proposal is to do deeply research on patent knowledge evolution based on text mining from the viewpoint of knowledge subject and object. Firstly, the domain ontology is constructed using users demand, and it is used for term identification and query expansion. The domain patent dataset is retrieved from SIPO(Chinese Patent Database) with query. Secondly, The representation of patent knowledge is presented with the form of < Feature,Effect > and < Effect，Value > in order to convert the unstructed information into knowledge unit with metrics , and the key issue is to filled the framework of knowledge using named entity identification, attribution extraction and semantic annotation. The patent theme is clustered from similarity and novelty. Finally, competition network of knowledge subject and co-word network of knowledge object are built up, and the map of patent evolution is drawn by time dimension to discovery chance and forecast the trend. The visual platform of patent knowledge mining is developed.