Title: Exploring the evolution of emerging technology using text mining method based on machine learning: evidence from intelligent ship technology

Authors: Jingyi Yao; Weiwei Liu; Kexin Bi

Addresses: School of Economics and Management, Harbin Engineering University, Harbin, 150001, China ' School of Economics and Management, Harbin Engineering University, Harbin, 150001, China ' School of Economics and Management, Harbin Engineering University, Harbin, 150001, China

Abstract: Emerging technologies has reshaped multiple industries, notably the maritime sector, where intelligent ship technology has emerged as a pivotal innovation. However, little attention has been given to mapping its evolution. To address this gap, we introduce a framework, employing text mining and machine learning to unravel the evolution of intelligent ship technology. Our method applies LDA to identify topics over time, dissects evolution in intensity, content, and state, and maps evolution paths of topics to assess current research and forecast trends. The main findings are as follows. First, the topic distribution of intelligent ship technology gradually shows diversity over time. Second, the topic content shows crossover, penetration and integration among the research topics. Third, the evolution state presents complex evolutionary relationships of dividing, consolidating and inheritance. This extends research, offering a dynamic view of state and progress of intelligent ship technology, informing researchers, policymakers, and stakeholders to harness its potential.

Keywords: intelligent ships; latent Dirichlet allocation; LDA model; topic identification; topic evolution analysis; technology evolution path.

DOI: 10.1504/IJTIP.2025.150688

International Journal of Technology Intelligence and Planning, 2025 Vol.14 No.1, pp.1 - 30

Received: 05 Aug 2023
Accepted: 15 Jul 2024
Published online: 22 Dec 2025 *

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