Design of Intelligent Extraction Method for Key Electronic Information Based on Neural Networks

With the rapid development of the Internet and other emerging media, how to find the needed information from massive electronic documents in time and accurately has become an urgent problem. A key electronic information extraction method based on neural network learning ideas has been proposed to solve the problems of time-consuming and difficult deep semantic feature mining in traditional text classification methods. Firstly, a weighted graph model was introduced to improve the TextRank keyword extraction algorithm, helping to capture complex data information and implicit semantics. The results indicate that the optimization method has the highest extraction accuracy (96.52%) on the CSL dataset, and its performance in feature extraction of information data is superior to other comparative models. Secondly, combining LSTM and self attention mechanism to achieve key feature extraction of contextual semantic information. The results indicate that this optimization method has relatively small training and testing errors in data classification, and tends to converge in the later stages of iteration. The accuracy of information extraction reached 94.37%, which is better than other comparative models. The keyword extraction integrity of the fusion model on the THUCNews dataset and Sogou News dataset were 86.2 and 84.1, respectively, with consistency of 96.3 and 94.7, and grammatical correctness of 92.1 and 92.2, respectively. The neural network-based extraction method proposed by the research institute can not only effectively improve the accuracy of information extraction, but also adapt to the changing data environment, and has great potential for application in the field of electronic information processing.

@article{Chen2024,
  title     = {Design of Intelligent Extraction Method for Key Electronic Information Based on Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xiaoqin Chen and Xiaojun Cheng},
  doi       = {10.14569/IJACSA.2024.0150993},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150993}
}