As China’s economy continues to grow of informational technology and mobile Internet industry, the online tourism industry has received more and more extensive attention and use. However, as an emerging industry, users often need to spend a lot of time to choose travel services that match their needs because of the complex amount of relevant information. Under such circumstances, this paper studied the recommendation method in travel platform. First, the big data is used to extract user data. Secondly, the current online travel business recommendation for users has the problem of low accuracy. The reason is that the services provided are still in traditional recommendation algorithm. In this paper, the Bayesian network is used to evaluate the user’s attribute preference and generate a data model, using effective methods in artificial intelligence algorithms to improve collaborative filtering algorithms and finally generate hybrid recommendation algorithms. Compared with the traditional recommendation method, the experimental results showed that the research can improve the recommendation accuracy of tourist attractions by 6.55%, increase the user’s satisfaction for the platform, and enhance the visit rate and retention rate of the tourist attraction recommendation platform.

In this paper, the existing scenic spot recommendation algorithms ignore the implicit trust and trust transmission of users when dealing with user relationships, and the lack of historical browsing behavior data of users in new city scenes leads to an inaccurate recommendation. In this paper, a personalized scenic spot recommendation method combining user trust relationship and tag preference is proposed. Firstly, the trust degree is introduced when the recommendation quality is poor only considering the similarity of users. By mining the implicit trust relationship of users, the problem that the existing research cannot make recommendations when the direct trust is difficult to obtain is solved, and the data sparsity and cold start problems are effectively alleviated. Secondly, in the process of user interest analysis, the relationship between scenic spots and tags is extended to the relationship among users, scenic spots and tags, and users’ interest preferences are decomposed into long-term preferences for different scenic spots tags, which effectively alleviates the problem of poor recommendation quality when users’ historical tour records are lacking. The personalized tourism recommendation method proposed in this paper effectively integrates many features of social networks and effectively alleviates the problems of data sparseness and feature learning in tourism recommendation based on social networks by using vectorization and deep learning technology. Its research has very important usage scenarios and commercial value in the tourism industry. This model can efficiently mine the association rules between scenic spots in multisource information data. The experimental results show that mining the correlation between the scenic spots selected by tourists can provide effective information for tourism decision-making.

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After decades of development, China's tourism industry has made brilliant achievements, entering the period of mass tourism characterized by independent tourism and self-help tourism. Along with the vigorous development of Chinese tourism industry, the application and strong growth of the Internet technology, the online travel service market is becoming the most vital area which grows very fast. This paper aims to analyze the drawbacks of the existing sales model in tourism industry and a new mode of application in internet plus tourism in the process of tourism industry in the future, based on the main line of tourism industry which is the most vital area in the third industry at the background of internet distinguishing to the traditional tourism pattern characterized by E-Commercialization.