The analysis of rural revitalization serviceplatform in smart city under back propagation neural network.
To achieve rural revitalization and enhance the development of rural tourism, this study employs a back propagation neural network (BPNN) to construct a rural revitalization development model. Additionally, the Grey Relation Analysis (GRA) algorithm is used to classify rural revitalization efforts a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317702 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850128559634382848 |
|---|---|
| author | Gongyi Jiang Weijun Gao Meng Xu Mingjia Tong |
| author_facet | Gongyi Jiang Weijun Gao Meng Xu Mingjia Tong |
| author_sort | Gongyi Jiang |
| collection | DOAJ |
| description | To achieve rural revitalization and enhance the development of rural tourism, this study employs a back propagation neural network (BPNN) to construct a rural revitalization development model. Additionally, the Grey Relation Analysis (GRA) algorithm is used to classify rural revitalization efforts across different cities. Consistency testing is applied to analyze rural revitalization indicators, and a tourism service evaluation model is established to assess rural revitalization tourism services from the perspective of smart cities. The research results indicate that: (1) the training results and expected values of the ten cities are relatively consistent, and the classification of rural revitalization development is good; (2) The five major indicators of tourism information services, tourism security services, tourism transportation services, tourism environment services, and tourism management services all meet the consistency test, and the consistency test results are all less than 0.1, confirming the reliability and effectiveness of the research data; (3) The tourism information and management services are mainly evaluated at level C, accounting for 62% and 62.5% respectively. The tourism transportation and safety services are mainly evaluated at level D, and the model can indicate the level of rural revitalization tourism service; (4) Compared with other algorithms, the GRA-BPNN algorithm performs the best in rural revitalization evaluation, with an accuracy of 92.3%, precision of 91.8%, recall rate of 93.7%, and F1 score of 92.7%. This study optimizes the rural revitalization tourism service platform, enhances the quality of rural tourism, promotes the development of the rural tourism industry, and contributes to the realization of rural revitalization. |
| format | Article |
| id | doaj-art-3be072e2e5c348afba6cd7afe0362420 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-3be072e2e5c348afba6cd7afe03624202025-08-20T02:33:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e031770210.1371/journal.pone.0317702The analysis of rural revitalization serviceplatform in smart city under back propagation neural network.Gongyi JiangWeijun GaoMeng XuMingjia TongTo achieve rural revitalization and enhance the development of rural tourism, this study employs a back propagation neural network (BPNN) to construct a rural revitalization development model. Additionally, the Grey Relation Analysis (GRA) algorithm is used to classify rural revitalization efforts across different cities. Consistency testing is applied to analyze rural revitalization indicators, and a tourism service evaluation model is established to assess rural revitalization tourism services from the perspective of smart cities. The research results indicate that: (1) the training results and expected values of the ten cities are relatively consistent, and the classification of rural revitalization development is good; (2) The five major indicators of tourism information services, tourism security services, tourism transportation services, tourism environment services, and tourism management services all meet the consistency test, and the consistency test results are all less than 0.1, confirming the reliability and effectiveness of the research data; (3) The tourism information and management services are mainly evaluated at level C, accounting for 62% and 62.5% respectively. The tourism transportation and safety services are mainly evaluated at level D, and the model can indicate the level of rural revitalization tourism service; (4) Compared with other algorithms, the GRA-BPNN algorithm performs the best in rural revitalization evaluation, with an accuracy of 92.3%, precision of 91.8%, recall rate of 93.7%, and F1 score of 92.7%. This study optimizes the rural revitalization tourism service platform, enhances the quality of rural tourism, promotes the development of the rural tourism industry, and contributes to the realization of rural revitalization.https://doi.org/10.1371/journal.pone.0317702 |
| spellingShingle | Gongyi Jiang Weijun Gao Meng Xu Mingjia Tong The analysis of rural revitalization serviceplatform in smart city under back propagation neural network. PLoS ONE |
| title | The analysis of rural revitalization serviceplatform in smart city under back propagation neural network. |
| title_full | The analysis of rural revitalization serviceplatform in smart city under back propagation neural network. |
| title_fullStr | The analysis of rural revitalization serviceplatform in smart city under back propagation neural network. |
| title_full_unstemmed | The analysis of rural revitalization serviceplatform in smart city under back propagation neural network. |
| title_short | The analysis of rural revitalization serviceplatform in smart city under back propagation neural network. |
| title_sort | analysis of rural revitalization serviceplatform in smart city under back propagation neural network |
| url | https://doi.org/10.1371/journal.pone.0317702 |
| work_keys_str_mv | AT gongyijiang theanalysisofruralrevitalizationserviceplatforminsmartcityunderbackpropagationneuralnetwork AT weijungao theanalysisofruralrevitalizationserviceplatforminsmartcityunderbackpropagationneuralnetwork AT mengxu theanalysisofruralrevitalizationserviceplatforminsmartcityunderbackpropagationneuralnetwork AT mingjiatong theanalysisofruralrevitalizationserviceplatforminsmartcityunderbackpropagationneuralnetwork AT gongyijiang analysisofruralrevitalizationserviceplatforminsmartcityunderbackpropagationneuralnetwork AT weijungao analysisofruralrevitalizationserviceplatforminsmartcityunderbackpropagationneuralnetwork AT mengxu analysisofruralrevitalizationserviceplatforminsmartcityunderbackpropagationneuralnetwork AT mingjiatong analysisofruralrevitalizationserviceplatforminsmartcityunderbackpropagationneuralnetwork |