文章编号： 1672-5328（2026）01-0123-04

李毅喆1, 2
(1. 同济大学交通学院，上海201804；2.同济大学道路与交通工程教育部重点实验室，上海201804)

摘要： 选取来自国际学术期刊的论文，以概述形式对城市交通理论方法、实证分析等学术研究成果 进行总结性介绍，旨在增强城市交通业界和学界对国际学术动向和研究热点的关注，促进学术交 流。《利用海量短视频对上海城市形象进行多模态感知》一文以上海市为例，从空间、景观、社会 与情感4 个维度，构建多模态的城市意象感知框架，探讨短视频大数据在城市意象分析中的应用。 研究发现，上海市城市意象呈现“核心集聚-外围扩散”的空间结构；景观意象表现为现代都市、 传统建筑、自然景观与消费空间等多样化主题；情感分析显示，上海市总体情感偏正，但在新型冠 状病毒感染疫情期间波动显著。这一研究为城市规划、城市品牌建设及社会情绪管理提供了新的数 据支持和理论依据。

关键词： 城市意象；短视频；多模态；深度学习；情感分析；上海市

中图分类号： U491

文献标识码：A

Academic Dynamics on Urban Image Perception Based on Multi-Perspective Short Videos

Li Yizhe1, 2
(1. College of Transportation Engineering, Tongji University, Shanghai 201804, China; 2. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China)

Abstract： A review of selected papers from international academic journals is presented to summarize research findings, theoretical approaches, and empirical analyses of urban transportation. The aim is to enhance the communication between industrial and academic fields in urban transportation, highlight international research focuses, and promote academic exchange. Using Shanghai as a case, the article "Utilizing Massive Short Video Data for Multi-Perspective Perception of Shanghai's Urban Image" builds a multi-perspective framework from four dimensions: space, landscape, society, and emotion. It examines the application of short-video big data in urban image analysis. This study finds that Shanghai's urban image shows a spatial pattern of “core concentration and outward diffusion”. The landscape imagery include diverse themes, such as the modern metropolis, traditional buildings, natural scenery, and consumer spaces. Sentiment analysis shows that Shanghai generally has a positive emotional tone. However, the sentiment fluctuated significantly during the COVID-19 pandemic. This study provides new data support and a theoretical basis for urban planning, city branding, and social emotion management.

Keywords： urban imagery; short videos; multi-perspective; deep learning; sentiment analysis; Shanghai