Application of Computer Big Data Modeling Based on Lagrangian Mathematical Equation in the Village Cultural Industry Model

To understand the Lagrangian mathematical equation, the author proposes an application study of big data modeling in the village cultural industry model. The author takes 20 traditional villages in Beijing as the object, and through crawling relevant Internet cultural and tourism big data, using the...

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Veröffentlicht in:Applied mathematics and nonlinear sciences Jg. 8; H. 2; S. 2745 - 2754
Hauptverfasser: Zhang, Xiaodong, Peng, Changrong, Liu, Qian, Ma, Yichu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Beirut Sciendo 01.07.2023
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
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ISSN:2444-8656, 2444-8656
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Zusammenfassung:To understand the Lagrangian mathematical equation, the author proposes an application study of big data modeling in the village cultural industry model. The author takes 20 traditional villages in Beijing as the object, and through crawling relevant Internet cultural and tourism big data, using the theory of tourism consumer behavior, from the aspects of consumer motivation behavior, food, housing, shopping, and other experience behavior, consumer evaluation behavior, and consumer space-time behavior, carry out targeted content analysis, aggregation analysis, etc, fully reveal the behavioral portrait of tourists in Beijing’s traditional villages. The results show that the overall tourism experience of Beijing’s traditional villages is good, but the development level is quite different, the cultural and tourism supply pattern of “some have boutiques, some have highlights, and the overall influence is weak” is presented to improve the overall level. At the same time, the computer big data based on tourists’ feedback can reasonably reveal the inherent characteristics and problems of traditional village tourism, which is innovative in the current situation where village tourism data is difficult to obtain.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2444-8656
2444-8656
DOI:10.2478/amns.2023.1.00454