Based On The Technology Of Web Search Visitors Forecast Method Research

Along with the rapid popularization of Internet technology, and the vigorous development of tourism industry, the Internet technology has been widely used in tourism industry. As an important information platform, the Internet has been used by many tour operators, tourism enterprise and tourism government to issue travel information, and the tourists can also obtain important tourism information from it.The study of social behavior based on network information technology shows that there is a definite relation between network information flow and the reality of social behavior. The network information flow plays a guiding role in social behavior decision-making, and has a precursor effect on it. The travel behavior is included in this social behavior. According to the attention of key words in different netizens, Google and Baidu companies supply the function of Google search index and Baidu index which are based on web search and news search to provide the mass data analysis service for free. They can also reflect attention of the key words in some period of time and provide the network search volume data of every keyword from the year2006to now. This paper shows the relationship between Internet search amount and tourist amount with the qualitative and quantitative methods. With establishing analysis forecasting models, tourist prediction method based on network search volume data has been studied to enrich and consummate the science of tourism and technology theory in the aspect of theory. On the basis of theory research, it can provide some reference in the practice of the tourist enterprise and tourism destinations to make the tourism sustainable plans.This paper based on tourism behavior, network information technology and prediction model theories, and through reading literatures in tourist-predicting, social and tourist behavior based on information technology, summarizes the research from home and abroad and it can be found that there is a definite relationship between the amount of tourist and network attention. Therefore, combined with the research needs of theory reality, the topic will be determined as prediction method research based on Internet search technology. According to research needs of visits prediction method in this study, a questionnaire about tourists’network behavior has been compiled to survey the network behavior of tourists and explore the degree, concern and their time of tourist to tourist information network in order to provide the reference to the selection of key words. Selecting Summer Palace and Mount Tai as the empirical research object, the method of choosing network keywords has been explored in different time scales. Analyzing the relevant relationship between keyword search volume data and the amount of tourists, I explore the method of setting up forecasting model that based on the network search keywords volume data. The consultations are as the follow. Firstly, with the regard to the access of those visitors obtain tourism information before starting to travel, there are more than80%of respondents being from Internet advertising report and introduces. Therefore, the search volume of relevant network keywords could reflect the real number of visitors in some extent. The time that the tourists obtain tourism information before starting to travel is within a week. The route, expenses and the weather of the tourism destinations is the universal concern and the information that they will inquire. The public praise and traffic play a certain influence on the tourists as well. Secondly, there is strong relevance between keywords to search for data and the number of tourists and most of them are positive correlation. It mainly depends on the behavior of tourist that is relevant travel information search and attention before they travel. Thirdly, according to the information that tourists concern to tourism destination before they travel, in the process of choosing the primary key words, the theories of tourism, tourist behavior and the consultation from the questionnaire should be considered. We choose the keywords which relate to the name and pictures of the tourism destination and the cities, tour route, ticket prices, weather conditions and lodging situation. Based on this, the keywords can be confirmed using the Internet search engine and benchmarks relevant keywords recommend function. Fourthly, determent of the final keywords should refer to the relevance between the primary keywords to search volume and the number of tourists. The research shows that taking the keywords which have strong relevance with the number of tourists as the model parameter can reduce the error of prediction and improve the accuracy of the prediction.