Chinese Patent Clustering Analysis For Technical Problems

The extraction of patent information and the construction of corresponding models help to grasp the relevant technical knowledge and thus promote the innovation and development of products in the corresponding fields.At present,the analysis and research on Chinese patent information mainly focuses on the patent title,classification number,abstract,etc.The lack of analysis of the main content of patents leads to a certain limitation of patent analysis.This paper expands the field of patent analysis,and analyzes Chinese patents for technical problems.It can more clearly understand the problems faced in the process of patent innovation and solve problems,thus providing ideas for new innovations,promoting the process of innovation,and further Improve the efficiency of subsequent patent analysis.This article will take the patent background technical text as the technical question to extract the object,obtains the problem description sentence by summarizing the sentence pattern characteristic and constructs the problem dictionary match,and draws the viewpoint information extraction method to realize the Chinese patent technology question extraction,divides the technical question extraction to extract the question word,problem objects,problem units three processes.Firstly,it constructs problem word sets by iteratively extracting association rules and conditional random field models,and then unsupervised and supervised methods are combined.Unsupervised method results are used as input for supervised methods to extract problem objects.Finally,the extraction of problem units is regarded as The sequence labeling problem is characterized by the extracted problem words and problem objects,and a multi-feature template is established,and the extended problem unit quad is extracted.In order to improve the traditional K-means text clustering algorithm,a text clustering algorithm based on similarity centers,called cK-means,was proposed to analyze Chinese patents for technical problems.The patent problem model built by clustering laid the foundation for the design and implementation of the patent recommendation system in the future.Experiments prove that the technical problem extraction method in this paper reduces the amount of manual labeling and achieves considerable results.The F1 value of the problem unit using the multi-feature template has reached 81.76%,and the stability of the patent problem model built in this paper is better than that of the traditional construction model.Accuracy has been improved,and both the F1 value and the accuracy rate obtained have increased by about 10%.Therefore,this method has a certain significance in the analysis of patented technical issues.