Patent Data Analysis Algorithm Design And Python Package Implementation

With the development of science and technology,the data volume of various industries is also increased.The patent information set is the largest technical information set in the world,which covers almost all the technical achievements in the application field.Patent data has become an inexhaustible technical literature and knowledge repository.However,such huge information resources are far from being utilized by people.It is of great practical significance to develop valuable patent knowledge from the sea of patent information and transform it into effective competitive intelligence.In order to analyze patent information from multiple perspectives,we strive to explore the deeper knowledge contained in patent information.This paper takes the patent data in the field of energy conservation and emission reduction technology in the steel industry as an example,and mainly conducts patent data analysis from the four aspects of trend analysis,cluster analysis,correlation analysis and citation analysis.For example,k-means clustering algorithm is adopted to mine the patent text,and Apriori correlation algorithm is adopted to mine the IPC classification number.On this basis,the process of patent data analysis is packaged and published.In the future process of patent data analysis,Python packages written can be directly invoked to realize the data analysis function conveniently and quickly and improve the analysis efficiency.