@Article{iasc.2020.013939,
AUTHOR = {Hao Peng, Qiao Li},
TITLE = {Research on the Automatic Extraction Method of Web Data Objects Based on Deep Learning},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {3},
PAGES = {609--616},
URL = {http://www.techscience.com/iasc/v26n3/40020},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2020.013939}
}