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Competitiveness Evaluation of Tourist Attractions Based on Artificial Neural Network

Received: 3 June 2020 Accepted: 17 September 2020 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 fourlayer 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.