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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, email

Intelligent Automation & Soft Computing 2020, 26(3), 609-616.


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.


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.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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