In recent years, tourism has rapidly developed into a key economic sector, with travel route recommendation algorithms playing a vital role in enhancing tourists’ experiences. These algorithms often utilize large amounts of travel data, social networks, and interest hotspots maps to recommend optimal routes. Social networks, as online platforms for communication and content sharing, help connect people, while interest hotspots maps visualize popular topics on social media. This paper proposes a tourism route recommendation algorithm based on social networks and interest hotspots maps, combining tourist preferences and scenic spot data. By analyzing tourist needs and scenic spot conditions, the algorithm improves route recommendations, reducing analysis time and increasing accuracy. Research results show that, before using this algorithm, tourists rated travel time, routes, and attractions at 87.25, 86.84, and 88.62 points, respectively. After using the travel route recommendation algorithm, tourists’ satisfaction was 95.76 points, 96.48 points and 92.89 points respectively. These results can showed that the travel route recommendation algorithm can improve the satisfaction of tourists, and that the research of travel route recommendation algorithm based on social networks and interest hotspots map was of practical value. This also provided a new research path for tourism route recommendation technology.