Classification Algorithm for Library Electronic Documents Based on Continuous Attribute

The automatic classification of document data will occupy an increasingly important position in digital libraries. Generally, the kernel method based on support vector machine is used to classify literature data on the standard test set, which has some shortcomings. In order to solve these problems, vocabulary expansion is used to preprocess the document vector to obtain a small but precise, orthogonal, and unambiguous new document vector; the document vector is sorted according to semantics to improve the access and calculation speed; the document is mapped to Lz with the help of wavelet kernel space for document classification. This paper analyzes the existing continuous attribute discretization methods in detail, discusses how to reduce the loss of information in the discretization process, and proposes a low-frequency discretization (LFD) algorithm based on the attribute low-frequency region. This method effectively reduces data loss by setting the segmentation point in the attribute interval with lower frequency, and through the research and analysis of the existing association rule mining algorithm, this paper combines low-frequency discretization, weighted multiple minimum support, and full confidence, and a weighted multiple minimum support association rule mining algorithm based on low-frequency discretization (WM-SamplingHT) is proposed. The algorithm first uses the low-frequency discretization algorithm to discretize the continuous attributes, then sets the respective weights and minimum support for the data items when mining frequent itemsets, removes the false patterns through the full confidence, and then obtains cleaner frequent itemsets. Using the real classification data of China Academic Journals Network, it is verified from the perspectives of abstract information and full-text documents. The results show that this method is superior to the nuclear method and has certain theoretical research and practical applications.