Automatic Recognition and Labeling of Knowledge Points in Learning Test Questions Based on Deep-Walk Image Data Mining

This paper deeply studies and discusses the application of image data mining technology based on the Deep-Walk algorithm in automatic recognition and annotation of knowledge points in learning test questions. With the rapid development of educational informatization, how to effectively mine and label the knowledge points in learning test questions from image data has become an urgent problem to be solved. In this paper, we introduce a novel approach that integrates graph embedding technology with natural language processing techniques. Initially, we leverage the Deep-Walk algorithm to embed the knowledge points present in the test question images, effectively transforming the high-dimensional image data into a low-dimensional vector representation. This transformation meticulously preserves the intricate structural information while meticulously capturing the subtle semantic nuances embedded within the image data. Subsequently, we undertake a thorough semantic analysis of these vectors, seamlessly integrating natural language processing techniques, to facilitate automated recognition with unparalleled precision. This innovative methodology not only elevates the accuracy of knowledge point recognition to new heights but also achieves semantic annotation of these points, thereby furnishing richer, more insightful data support for subsequent intelligent education applications. Through experimental verification, the proposed method has achieved remarkable results on multiple data sets, which proves its feasibility and effectiveness in practical applications. Furthermore, this paper delves into the expansive potential applications of this methodology in the realm of image data mining, encompassing areas such as online education, intelligent tutoring systems, personalized learning frameworks, and numerous other domains. As we look ahead, we aim to refine the algorithm, enhance recognition accuracy, and uncover additional application scenarios, thereby contributing significantly to the intelligent evolution of the education sector.

@article{Chang2024,
  title     = {Automatic Recognition and Labeling of Knowledge Points in Learning Test Questions Based on Deep-Walk Image Data Mining},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ying Chang and Qinghua Zhu},
  doi       = {10.14569/IJACSA.2024.0150915},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150915}
}