Association Rule Mining for English Digital Archive System Based on Improved Apriori Algorithm

Authors

  • Xiaoling Fang School of Foreign Languages, Chongqing College of Humanities, Science and Technology, Chongqing, 40000, China

Abstract

With the widespread use and implementation of digital technology, the English digital archive system has accumulated a significant amount of data encompassingvarious dimensionssuchaslearning behavior, teachingprocesses, andstudentfeedback. Extracting valuable information andknowledge from this vast data has now become crucial for educational research and management. To this end, it is suggested that an enhanced convolutional neural network be combined with an Apriori algorithm to design and optimize a digital archive management system and association rule mining for English language. The improved Apriori algorithm takes into account the data’s peculiarities and mining demands, thereby yielding comprehensible and high-quality results. The study’s outcomes revealed that when the system reached a maximum iteration of 114 times, the proposed method attained the highest fitness value of 98.25 on the training set, in comparison with other fitness values. Similarly, when the proposed method was tested on the validation set, it achieved a fitness value of 98.74 after reaching a maximum iteration of 61 times, compared to fitness values obtained by other methods. The change in the overfitting curves shows that the model’s performance in managing the data was stabilized after 30 iterations. With the optimized training model, the accuracy of data manipulation progressively increased to a consistent 90% and eventually converged to 99.99%. In practical applications, the test scores obtained by the proposed system for data transmission, preservation, retrieval, and management, all surpassed
84 points–a significant improvement. Notably, the system’s data management score exceeded 92 points. The research findings demonstrate the clear advantages of the new methodology over the traditional approach in regards to accuracy and operational efficiency. It also indicates the proposed method’s capacity to effectively manage the vast amount of data in the English digital archives of colleges and universities, thus yielding robust data support for research and management purposes in English education.

Keywords: Improved Apriori algorithm; English language; digital archiving system; association rules; educational management

Cite As

X. Fang, " Association Rule Mining for English Digital Archive System Based on Improved Apriori Algorithm",
Engineering Intelligent Systems, vol. 33 no. 2, pp. 131-140, 2025.

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Published

2025-03-01

Issue

Vol. 33 No. 2 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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