Open Access

ARTICLE

Construction of Intelligent Recommendation Retrieval Model of FuJian Intangible Cultural Heritage Digital Archives Resources

Xueqing Liao^*

Quanzhou University of Information Engineering, School for Creative Studies, Quanzhou, Fujian, 362000, China

* Corresponding Author: Xueqing Liao. Email:

Intelligent Automation & Soft Computing 2023, 37(1), 677-690. https://doi.org/10.32604/iasc.2023.037219

Received 27 October 2022; Accepted 06 February 2023; Issue published 29 April 2023

Abstract

In order to improve the consistency between the recommended retrieval results and user needs, improve the recommendation efficiency, and reduce the average absolute deviation of resource retrieval, a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed. The TG-LDA (Tag-granularity LDA) model is proposed on the basis of the standard LDA (Linear Discriminant Analysis) model. The model is used to mine archive resource topics. The Pearson correlation coefficient is used to measure the relevance between topics. Based on the measurement results, the FastText deep learning model is used to achieve archive resource classification. According to the classification results, TF-IDF (term frequency–inverse document frequency) algorithm is used to calculate the weight of resource retrieval keywords to achieve resource retrieval, and a recommendation model of intangible cultural heritage digital archives resources is built through the knowledge map to achieve comprehensive and personalized recommendation of resources. The experimental results show that the recommendation and retrieval results of the proposed method are more in line with users’ needs, can provide users with personalized digital archive resources, and the average absolute deviation of resource retrieval is low, the recommendation efficiency is high, and the utilization effect of archive resources is effectively improved.

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Keywords

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