Title: Building a tourism decision support system based on big data

Authors: Li Fu; Yi Yao

Addresses: School of Economics and Management, Taiyuan Normal University, Jinzhong, Shanxi, China; Shanxi Key Laboratory of Earth Surface Processes and Resource Ecology Security in Fenhe River Basin, Taiyuan Normal University, Jinzhong, Shanxi, China ' School of Economics and Management, Xinzhou Normal University, Xinzhou, Shanxi, China

Abstract: This paper studies the construction of a tourism decision support system based on Big Data (BD) technology and deep learning models. Apache Kafka is a pipeline for real-time data streams to stream data from different sources to the processing system. Apache Flink is a stream processing engine to processes and analyses the real-time incoming data streams and identifies emergencies. The Long Short-Term Memory (LSTM) network model receives data streams from Flink and performs time series prediction based on the user's historical data and real-time information. The output prediction results are used for travel recommendations through a collaborative filtering algorithm. The research results show that compared with the rules-based and collaborative filtering systems, the retention rate of the system implemented in this paper is higher than in the other two systems. This study enhances tourism decision support systems' personalisation and real-time response capabilities.

Keywords: tourism decision support system; big data technology; deep learning models; real-time response; personalised recommendations.

DOI: 10.1504/IJCAT.2026.151372

International Journal of Computer Applications in Technology, 2026 Vol.78 No.1, pp.1 - 12

Received: 11 Mar 2025
Accepted: 26 Jun 2025
Published online: 26 Jan 2026 *

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