Construction and Analysis of Surrounding Travel Demanding Graph Based on Dual Contrastive Learning Text Classification and Graph Neural Network

ABSTRACT

Understanding the main information about the current situation of the tourism market has become an urgent need and new trends in the development of the tourism market. In this paper, we use natural language processing technology to analyze the development of tourism around Maoming City, Guangdong Province during the COVID-19 epidemic by means of data mining methods to build a local tourism graph, refine and design models and methods such as RoBERTa-BiGRU-Attention fusion model, dual contrastive learning, BERT-BiLSTM-CRF named entity identification technique, improved Apriori algorithm, GNNLP model based on conventional models and proved the rationality and efficiency of the improved model by comparative test, provide oriented suggestions to help government departments promote tourism and tourism enterprises product supply, optimize resource allocation and explore the market constantly during the epidemic period after scientific analysis and summary. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)