Tourism destination competitiveness evaluation model integrating multi-source big data and machine learning

Abstract

The intelligent Internet of Things has played a certain role in the tourism industry. In the evaluation of tourist destination competitiveness, the text evaluation and images of tourist destination can be collected through the Internet of Things. Among them, text data processing is relatively simple, but image and video processing is more difficult, and different data sources will lead to problems such as the decline of federated learning algorithms. In order to improve the data processing problem in the evaluation of tourist destination competitiveness and to solve the problem of unbalanced utilization of computing and communication resources caused by system heterogeneity, this paper proposes an adaptive asynchronous aggregation method Adaptive asynchronous aggregation method based on outdated threshold control (HiFedCNM) based on obsolescence threshold control. The experimental results show that the algorithm outperforms some existing excellent algorithms in model training accuracy, computational efficiency, communication efficiency and system cost. In addition, this paper proposes a conceptual model of tourism destination competitiveness. Through the case study, it can be seen that the model proposed in this paper can play a certain role in the analysis of tourist destination competitiveness. At the same time, the model method proposed in this paper can provide a reliable reference for the subsequent heterogeneous fusion of tourism Internet of Things data, and can provide a reliable method for evaluating the competitiveness of tourism destinations.