Value and path optimization of multi-data fusion algorithm to help sports tourism high-quality development-Reference-Cited by-同舟云学术

Value and path optimization of multi-data fusion algorithm to help sports tourism high-quality development

Published:2023-11-04 Issue:1 Volume:9 Page:
ISSN:2444-8656
Container-title:Applied Mathematics and Nonlinear Sciences
language:en
Short-container-title:

Author:

Cheng Jiawen¹,Xu Zhongwei¹,Li Ze²

Affiliation:

1. 1 School of Tourism Sciences , Beijing International Studies University , Beijing , , China .

2. 2 School of Recreation Sport and Tourism , Beijing Sport University , Beijing , , China .

Abstract

Abstract This paper begins by analyzing the high-quality development of sports tourism and then characterizes the massive data in sports tourism with multi-source heterogeneous and heterogeneous data. The parallel data fusion platform is Hadoop, and the multi-data feature extraction algorithm is LSTM. To complete multi-source data fusion, a random forest model enhances the algorithm’s classification performance. It is verified that the information weight value H in the weight of high-quality development of sports tourism gradually increases and stabilizes at 9.87. The multi-source data fusion algorithm can help in the in-depth fusion and common sharing of data resources in sports tourism and promote the high-quality development of sports tourism.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Link

https://www.sciendo.com/pdf/10.2478/amns.2023.2.00947

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