Digital footprints converge into a complex individual and group behavior picture, which truly reflects the user’s choice preference and deep-seated behavior law. However, its application in tourism needs to be further explored. Based on the core characteristics of consumption footprint, this paper applies it to tourism field to analyze tourists’ consumption behavior based on the theory of digital footprint and consumer behavior. This study aims at mining text data, analyzing its characteristics, creating a digital footprint integrated learning model and developing the mining and analysis technology of tourism digital footprints. This method can improve the accuracy of consumer decision-making tendency prediction, and the prediction results can be used to formulate targeted consumption strategies, so as to effectively stimulate consumption vitality and improve consumer satisfaction.