Nowadays, the technology of "Internet plus" is developing very rapidly. Its cooperation with education promotes the innovative development of educational reform practice, improves the vitality of education. The cooperation of online and offline education has innovated teaching ideas and changed the teaching situation. It fully enhances students' proactive practical skills, thereby improving their comprehensive quality level, developing new educational concepts, and promoting innovation in new teaching models. Tourism behavior prediction based on social network analysis employs social network analysis techniques to predict the behavior patterns of tourists in different stages of their travel, such as destination selection, itinerary planning, and activity participation. By analyzing the behavior patterns of tourists on social media platforms, social network analysis algorithms can identify the factors that influence their decision-making, such as personal preferences, social influence, and situational context, and generate personalized recommendations to improve their travel experience. For example, if a tourist has shown a preference for outdoor activities on social media, tourism behavior prediction systems can recommend relevant activities and destinations that match their interests. These systems can also incorporate real-time data, such as weather forecasts and traffic conditions, to adjust recommendations accordingly. Overall, tourism behavior prediction based on social network analysis can provide tourists with more personalized and satisfactory travel experiences, while also promoting the development of local tourism industries.