The Method Of Mining Travel Intention Based On The Content Of Travelogs

In recent years,the national policy support and the progress of science and technology promote the rapid development of China's tourism.With the development of big data and cloud computing technology,personalized travel recommendation,intelligent customer service,real-time recommendation and other intelligent services become reality.The new epidemic situation has brought certain challenges to the tourism industry,but also promoted the development of smart tourism.Intelligent tour guide,online booking,VR tourism and other intelligent tourism services are gradually popularized.In the post epidemic period,people's willingness to travel is rising.The tourism industry should still grasp the tourism service and experience to promote the rapid recovery of tourism.Accurate and real-time personalized recommendation is an important part of intelligent tourism service,which can effectively improve user satisfaction.The premise of tourism recommendation is to have a deep understanding of tourists' tourism needs and establish a tourist interest model.The establishment of interest model is generally based on the tourists' record behaviors such as searching,browsing and commenting in the platform,as well as the information such as pictures and location check-in issued by tourists to analyze the tourist attractions and tourist routes.This kind of method can only know which scenic spots the tourists have been to,but can not mine the specific tourism activities in fine granularity.UGC content such as travel notes contains a lot of high-value information,such as tourists' travel path,specific tourism activities,and feelings in the journey.These fine-grained information can tap the deep-seated internal needs of tourists and achieve accurate tourism recommendation.At present,there are few researches on travel notes,and the research depth is shallow,which only stays at the level of structured information.Therefore,this paper will mine tourism activities and emotional information based on travel notes,build a tourist behavior model based on event map as knowledge representation,and establish a tourism intention prediction model to deeply mine tourism intention and demand,and improve the effect of personalized tourism recommendation.First of all,this paper puts forward a tourist behavior model based on tourism activities and emotions,which is represented by the event map to reflect the dynamic changes of tourists' tourism activity path and emotion.Based on the part of speech rules and dependency relations,a rule template is constructed to extract the emotion corresponding to the related activities,and finally the dynamic tourist behavior model is represented by the event map.Secondly,a tourism intention prediction model based on convolution neural network is constructed.Through the combination of topic model and literature research,the tourism intention category system is defined as the label set of the prediction model.A three channel vector matrix representing the spectrum characteristics of the behavior model is constructed as the input layer to realize the prediction of tourists' multiple tourism intentions.Compared with the traditional textcnn and ml-knn models,the prediction model has better performance and higher F1-score.