Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network
This paper analyzes the deficiencies of human resource allocation in the tourism industry by investigating the human resource allocation in the tourism industry, puts forward corresponding improvement measures and suggestions, and strives to provide certain guidance and helpful effects for the const...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/1332829 |
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| author | Shuo Zhu Yan Liu |
| author_facet | Shuo Zhu Yan Liu |
| author_sort | Shuo Zhu |
| collection | DOAJ |
| description | This paper analyzes the deficiencies of human resource allocation in the tourism industry by investigating the human resource allocation in the tourism industry, puts forward corresponding improvement measures and suggestions, and strives to provide certain guidance and helpful effects for the construction of tourism resource informatization. In this paper, a modified BP neural network model is proposed by introducing random perturbation terms on the hidden layer in the BP neural network algorithm, and the weight matrix connecting the input values is added with the random perturbation matrix to obtain a new weight matrix so that the convergence effect of the improved BP neural network algorithm is improved. Then, to address the problem that the initial weights of the long and short-term memory neural network and gated BP unit neural network have a large impact on the convergence speed and prediction accuracy of the algorithm after the initial weight selection is determined, this paper introduces the random perturbation term into the gate structure of the long and short-term memory neural network and gated BP unit neural network and proposes and connects an improved long and short-term memory neural network and gated BP unit neural network. The weight matrix of the input values is added with the random perturbation matrix to obtain the new weight matrix so that the convergence effect of the improved long and short-term memory neural network algorithm and the gated BP unit neural network algorithm is improved. Constructing the human resource allocation model of the tourism industry and proposing coping strategies and countermeasures and taking the human resource allocation system of the tourism industry as the core, the human resource allocation model of the tourism industry is established by combining the network image crisis life cycle system of tourism scenic spots and the network public opinion dissemination model. From the perspective of managers, the human resource allocation management policy and management procedures of the tourism industry are proposed. Using the quantifiable and disenable characteristics of online text information, the response strategy of online monitoring and propaganda and offline management and enhancement is proposed, and innovative countermeasures to the human resource allocation of the tourism industry are proposed in three categories: network originated, reality coexisting, and reality originated. Through this paper, we propose a new approach to human resource allocation management and development in the tourism industry and improve the efficiency of human resource allocation in the tourism industry. |
| format | Article |
| id | doaj-art-494f8cde5fa642edbc3b3b94ea028820 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-494f8cde5fa642edbc3b3b94ea0288202025-02-03T01:04:45ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/1332829Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural NetworkShuo Zhu0Yan Liu1Tourism CollegeHuihua CollegeThis paper analyzes the deficiencies of human resource allocation in the tourism industry by investigating the human resource allocation in the tourism industry, puts forward corresponding improvement measures and suggestions, and strives to provide certain guidance and helpful effects for the construction of tourism resource informatization. In this paper, a modified BP neural network model is proposed by introducing random perturbation terms on the hidden layer in the BP neural network algorithm, and the weight matrix connecting the input values is added with the random perturbation matrix to obtain a new weight matrix so that the convergence effect of the improved BP neural network algorithm is improved. Then, to address the problem that the initial weights of the long and short-term memory neural network and gated BP unit neural network have a large impact on the convergence speed and prediction accuracy of the algorithm after the initial weight selection is determined, this paper introduces the random perturbation term into the gate structure of the long and short-term memory neural network and gated BP unit neural network and proposes and connects an improved long and short-term memory neural network and gated BP unit neural network. The weight matrix of the input values is added with the random perturbation matrix to obtain the new weight matrix so that the convergence effect of the improved long and short-term memory neural network algorithm and the gated BP unit neural network algorithm is improved. Constructing the human resource allocation model of the tourism industry and proposing coping strategies and countermeasures and taking the human resource allocation system of the tourism industry as the core, the human resource allocation model of the tourism industry is established by combining the network image crisis life cycle system of tourism scenic spots and the network public opinion dissemination model. From the perspective of managers, the human resource allocation management policy and management procedures of the tourism industry are proposed. Using the quantifiable and disenable characteristics of online text information, the response strategy of online monitoring and propaganda and offline management and enhancement is proposed, and innovative countermeasures to the human resource allocation of the tourism industry are proposed in three categories: network originated, reality coexisting, and reality originated. Through this paper, we propose a new approach to human resource allocation management and development in the tourism industry and improve the efficiency of human resource allocation in the tourism industry.http://dx.doi.org/10.1155/2022/1332829 |
| spellingShingle | Shuo Zhu Yan Liu Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network Journal of Mathematics |
| title | Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network |
| title_full | Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network |
| title_fullStr | Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network |
| title_full_unstemmed | Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network |
| title_short | Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network |
| title_sort | analysis of human resource allocation model for tourism industry based on improved bp neural network |
| url | http://dx.doi.org/10.1155/2022/1332829 |
| work_keys_str_mv | AT shuozhu analysisofhumanresourceallocationmodelfortourismindustrybasedonimprovedbpneuralnetwork AT yanliu analysisofhumanresourceallocationmodelfortourismindustrybasedonimprovedbpneuralnetwork |