Urban agglomeration is the mainstream trend of urban development in the world. It is also the main form of new urbanization in China and an important platform to participate in international competition and cooperation. The pattern of industrial division of labor has basically taken shape in Chengdu Plain Economic Zone, and the industrial cooperation system has been gradually established. However, the phenomenon of industrial isomorphism is still prominent. In the process of promoting coordinated industrial development, there are still some problems such as disunity of understanding, imperfect mechanism, and imperfect environment. The regional economic potential is influenced by too many entities and the dynamic changes of economic structure, and the change ratio is highly nonlinear. In this paper, a MLR-GCD (Multiple Linear Regression Grey Correlation Degree) prediction model for the development trend of Chengdu Plain Economic Zone is proposed. In the decision-making process, MLR (multiple linear regression) method is introduced to construct the GCD (Grey Correlation Degree) of training economic-related data set, and then the GCD is pruned to transform it into standard decision-making data. The experimental results show that compared with other prediction models, the improved model has higher accuracy of regional economic prediction, can quickly and accurately predict the development potential trend of Chengdu Plain Economic Zone, and has important application value.
The consistency of tourists’ perceptions of a destination and the public’s evaluation of that perception is the foundation of destination image construction. In this paper, the principal component analysis method is used to analyze the tourism competitiveness of Leshan based on the wireless communication platform, and the obtained multisource geographic data are clustered according to the theme, using LDA (latent Dirichlet allocation) document theme generation model and topic clustering technology of
K
-means clustering algorithm. The hot events and hot topics that tourists care about in scenic spots are quickly extracted during the time span of this study. The model shows that tourists’ destination image perception is an important antecedent variable of tourists’ behavior intention, and perceived value and local attachment are two important intermediary variables. As for the overall effect of tourists’ behavior intention, tourists’ destination image perception has the greatest effect, followed by perceived value, and local attachment is the smallest.
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