Open Access
ARTICLE
Research on the Automatic Extraction Method of Web Data Objects Based on Deep Learning
Hao Peng*, Qiao Li
School of Information Science and Engineering, Hunan International Economics University, Changsha 410205, China
Address: Lu GuYuan, High-tech Industrial Development Zone, Yuelu District, Changsha City, Hunan Province
* Corresponding Author: Hao Peng,
Intelligent Automation & Soft Computing 2020, 26(3), 609-616. https://doi.org/10.32604/iasc.2020.013939
Abstract
This paper represents a neural network model for the Web page information
extraction based on the depth learning technology, and implements the model
algorithm using the TensorFlow system. We then complete a detailed
experimental analysis of the information extraction effect of Web pages on the
same website, then show statistics on the accuracy index of the page
information extraction, and optimize some parameters in the model according
to the experimental results. On the premise of achieving ideal experimental
results, an algorithm for migrating the model to the same pages of other
websites for information extraction is proposed, and the experimental results
are analyzed. Although the overall effect of the experiment is not as good as
that of the page information extraction in different websites, it is far more
effective than that of using the model directly on new websites. A new method
is proposed to improve the portability of the information extraction system
based on machine learning technology. At the same time, the deep nonlinear
learning method of the depth learning model can prove deeper features, can
have a more essential description of the abstract language, and can better
express and understand sentences from the syntactic and semantic levels.
Keywords
Cite This Article
H. Peng and Q. Li, "Research on the automatic extraction method of web data objects based on deep learning,"
Intelligent Automation & Soft Computing, vol. 26, no.3, pp. 609–616, 2020.
Citations