Research on a Journal Web Page Data Mining Decision Tree Algorithm for New Quality Productive Forces
At present,the journal websites and clustered journal platforms in China are mostly passive paper retrieval and down-load with a single structure and function or simply list knowledge structure diagrams.The intelligent data mining and active ac-quisition of high-value information on journal web pages have not yet been developed.This article introduces the current re-search status of intelligent data mining on foreign journal web pages,the basic concepts and related theories of autocorrelation functions,and proposes a decision tree algorithm for journal web page data mining using autocorrelation functions.Simulation experiment shows that by reducing the running time of CPU and I/O,the optimal acceleration ratio is obtained under the same number of processors,and the access to journal web data nodes is more comprehensive,improving data query efficiency.It pro-vides a personalized and efficient retrieval tool for intelligent information query in journal web data mining.With the all-round and full process penetration of data products and information products in new production relations and production links,it will also provide important support for the development of new quality productive forces.
journal web pagedata miningautocorrelation functiondecision tree
万方数据
维普
