Prediction of Sino-Korean cultural product trade based on one-dimensional convolutional neural network and support vector machine

Article type: Research Article

Authors: Jia, Liu^{; *}

Affiliations: Liaoning University of International Business, Dalian, China

Correspondence: [*] Corresponding author. Liu Jia, Liaoning University of International Business, Dalian, 116052, China. E-mail: [email protected].

Abstract: This study explores a predictive approach using a combination of a one-dimensional convolutional neural network and support vector machine to enhance the management of cultural product trade between China and South Korea, addressing the trade deficit challenge. The methodology involves the collection and categorization of diverse data related to the trade of cultural products between the two countries, identifying data mining directions. The research incorporates the design of association rule functions to identify viable data sources, and employs a hybrid data clustering algorithm integrating technology and spectral clustering to cluster available data. The features extracted from the data mining process are utilized as learning samples for trade prediction. Both a one-dimensional convolutional neural network and support vector machine are employed to model and predict cultural product trade between China and South Korea. Experimental results demonstrate the method’s accuracy in predicting trade situations under parameterized conditions. Throughout the prediction process, credibility measurement values and controllable correlation degrees consistently exceed 19 and 12.5, respectively, while uncertainty discrimination degrees and error coefficients remain below 12 and 6.

Keywords: Big data integration, Chinese and Korean cultural products, trade prediction, data mining, convolutional neural network, support vector machine

DOI: 10.3233/JIFS-238061

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024