This paper analyzes the power mechanism of tourism to help rural revitalization by elaborating the core content of rural tourism and rural revitalization. It also combines the advantages of the Lasso regression method and ridge regression method to form a flexible regression network model, which is used to predict the amount of tourists and tourists’ demand for rural tourism so as to put forward the implementation path of the tourism industry to help rural revitalization development. To verify the effectiveness of the proposed algorithm, data analysis is carried out in rural areas of L city as an example. The results show that compared with the Lasso regression model, the average absolute percentage error decreased by 2.41%, the average absolute error decreased by 2,233, and the root-mean-square error decreased by 2,967, which indicates that the elasticity regression network prediction model has a stronger generalization ability and a better prediction ability, and it can predict the number of tourists more accurately, and it can provide data for the tourism industry to contribute to rural revitalization and development. Reference.