Evidence map›Paper›PMID 40644389›Full record

ArticlePloS one2025

Evaluating visitor perception and spatial preferences of various museums based on machine learning from 2016 to 2024.

Yuandi Jiang, Kalyna Pashkevych, Shibo Bi

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yuandi JiangKyiv National University of Technologies and Design, Kyiv, Ukraine.ORCID https://orcid.org/0000-0001-6581-0950
Kalyna PashkevychKyiv National University of Technologies and Design, Kyiv, Ukraine.
Shibo BiSchool of Design art & Media, Nanjing University of Science and Technology, Nanjing, People's Republic of China.ORCID https://orcid.org/0009-0006-8944-9518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Museum architecture is essential for preserving cultural heritage. Understanding the spatio-temporal evolution of visitor preferences, image perceptions, and driving factors is vital for promoting cultural development. However, traditional methods such as questionnaires and interviews face challenges in elucidating how exhibition layouts, environmental facilities, and service quality affect visitor experience and satisfaction. In this study, 30 museums in 6 categories were selected as samples, and over 64,000 public online reviews from Dianping and Ctrip were selected as data sets. Kernel density and standard deviational ellipse methods revealed the spatio-temporal evolution of museum space preferences (2016-2024). TF-IDF and LDA algorithms identified key image perception themes. Visitor satisfaction was then evaluated with SnowNLP sentiment analysis to examine the dynamic correlation between the perception themes and satisfaction. The findings showed: 1) Museum visitors were highly concentrated in eastern coastal regions, with spatial distribution evolving from single-core to multi-core clusters, gradually expanding into central areas (e.g., Henan, Hubei, Shaanxi). 2) Museum image perception has shifted from object-centered to more human-centered experiences, with significant differences across the various categories. 3) Over 75% of visitors reported positive experiences, with ethnography museums showing the highest satisfaction in 2024 (Pro = 0.922), whereas history museums consistently had the lowest. 4) Satisfaction drivers were dynamic, with 85.26% of perception themes significantly correlated with satisfaction (p < 0.01), with rich collections, distinctive features, immersive experiences, and diverse visitation forms identified as the primary contributors to positive visitor experiences. Based on the comprehensive perspective of typology and spatio-temporal dynamic evolution, this study not only provides empirical support for museum space optimization, but also provides new ideas and strategies for functional research and methodological insights of public spaces.

Indexed as

Machine LearningMuseumsPerceptionHumansSurveys and Questionnaires

Identifiers

PMID40644389
PMCPMC12250188

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.