With the rapid development of tourism, tourists have become more aware of tourism level and quality. This triggers fierce competition between tourist attractions. To promote the core competitiveness of tourist attractions, this paper proposes a new evaluation model for the competitiveness of tourist attractions based on artificial neural network. First, a four-layer evaluation index system (EIS) was constructed for the competitiveness of tourist attractions, including detail elements, basic layer, core layer, and characterization layer. Next, all the evaluation indices were optimized through clustering by improved k-modes algorithm. Finally, a backpropagation neural network (BPNN) was established to evaluate the competitiveness of tourist attractions. Experimental results confirm the effectiveness of the proposed method. The research findings provide a reference for the application of artificial neural network (ANN) in other prediction fields.
The business management models are updated constantly, making it urgent to reform the teaching methods of business management courses (BMCs). Case-based teaching (CBT) is an important way to integrate the teaching and practice of business management. The construction and management of CBT resources has attracted the attention of scholars at home and abroad. This paper designed an information management system for the BMC case library. Firstly, the goals and content were summarized for the construction of the case library and the information management system. Next, the authors designed the BMC case library forms, as well as the function modules of the information management system. There are three core functions in the system: retrieval, cluster analysis, and course case allocation. At last, experimental results verified the effectiveness of the proposed case clustering algorithm and the good test performance of the established system.
For tourism industry, the ever-increasing energy consumption and the high carbon emissions are requiring close attention and prompt solution, thusly endowing the research on the energy-saving of buildings in tourist resorts very important and practical significance. For this reason, this study carefully considered the actual situations such the energy utilization method and the hygrothermal environment of tourist resorts and constructed a hygrothermal transfer model and the corresponding hygrothermal balance equation for buildings in tourist resorts; then, the paper proposed a few effective strategies for the energy-saving management of buildings in tourist resorts, and studied the annual energy consumption of tourist resorts and gave a building energy consumption analysis. At last, experimental results verified the rationality and effectiveness of the proposed energy consumption assessment and energy-saving management methods for tourist resort buildings in hygrothermal environment. This study provided a useful reference for the energy-saving methods of buildings in tourist resorts.
Building a sound system for assessing the training quality of high-class talents with talent introduction as the target is helpful for effectively analyzing the training mode of high-class talents in colleges and universities and discovering the underlying problems and weak links, thereby further optimizing the current mode, and improving the training level of high-class talents in the region. However, existing papers generally focus on macroscopic research of the training quality of high-class talents, so this paper attempts to study the training mode and quality view of high-class talents under the intervention of talent introduction policy. At first, this paper elaborated on the strategies for adjusting the training mode of high-class talents under the intervention of talent introduction policy, gave a diagram of the research model, and assessed the quality view of colleges and universities for talent training using four selected evaluation indexes, including solid knowledge base, independent research ability, rich practical ability, and sound personality and career outlook. Then, to figure out the changes in the training quality of different high-class talent training modes under the intervention of talent introduction policy, this paper built a high-class talent training quality prediction model based on Gated Recurrent Units (GRU) deep neural network, and gave the statistics of the prediction results of high-class talent training quality in the experiment. At last, this paper compared the differences in the quality view of high-class talent training of different colleges and universities under the intervention of talent introduction policy.
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