Article: Brand value fluctuation prediction and risk management of rural characteristic industries based on GAN-LSTM Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.48 pp.26 - 47 Abstract: This paper proposes a GAN-LSTM model for predicting brand value fluctuations and managing risks in rural characteristic industries. The model integrates generative adversarial networks to enhance limited data samples and long short-term memory networks to capture long-term dependencies in time-series data, improving prediction accuracy and robustness. Analysing a decade of data from traditional agriculture, tourism, handicrafts, and local food across multiple provinces, the study reveals distinct fluctuation patterns. Prediction errors were minimal, with handicrafts at -4.00% and local food at 3.16%. The GAN-LSTM model outperformed traditional and basic LSTM methods, reducing average prediction errors by 15% and 8%, respectively. It also provides quantified risk assessments, achieving up to 92% prediction accuracy and 90% risk management effectiveness. The findings offer theoretical guidance and practical support for the sustainable development of rural industries. Inderscience Publishers - linking academia, business and industry through research

Title: Brand value fluctuation prediction and risk management of rural characteristic industries based on GAN-LSTM

Authors: Ke Chen; Lina He

Addresses: School of Business Administration, Henan University of Animal Husbandry and Economy, Zhengzhou 45000, China ' School of Business Administration, Henan University of Animal Husbandry and Economy, Zhengzhou 45000, China

Abstract: This paper proposes a GAN-LSTM model for predicting brand value fluctuations and managing risks in rural characteristic industries. The model integrates generative adversarial networks to enhance limited data samples and long short-term memory networks to capture long-term dependencies in time-series data, improving prediction accuracy and robustness. Analysing a decade of data from traditional agriculture, tourism, handicrafts, and local food across multiple provinces, the study reveals distinct fluctuation patterns. Prediction errors were minimal, with handicrafts at -4.00% and local food at 3.16%. The GAN-LSTM model outperformed traditional and basic LSTM methods, reducing average prediction errors by 15% and 8%, respectively. It also provides quantified risk assessments, achieving up to 92% prediction accuracy and 90% risk management effectiveness. The findings offer theoretical guidance and practical support for the sustainable development of rural industries.

Keywords: GAN-LSTM model; fluctuations in brand value; risk management; rural characteristic industries.

DOI: 10.1504/IJICT.2026.153548

International Journal of Information and Communication Technology, 2026 Vol.27 No.48, pp.26 - 47

Received: 15 Dec 2025
Accepted: 19 Jan 2026
Published online: 13 May 2026 *

Title: Brand value fluctuation prediction and risk management of rural characteristic industries based on GAN-LSTM

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