Tourism Image Recognition and Scenic Spot Recommendation System Based on Deep Learning

The purpose of this article is to explore the application of DL (Deep Learning) in the intelligent development of tourism, and realize end-to-end tourism service by constructing a tourism image recognition and scenic spot recommendation system based on DL. In terms of methods, this article first collects and preprocesses large-scale tourism image data sets, and then designs a DL model for tourism image recognition, which is effectively trained and optimized. Then, combined with DL technology, a personalized scenic spot recommendation system is designed. Through system integration, image recognition and recommendation system are closely combined to provide users with all-round tourism services from image recognition to scenic spot recommendation. The experimental results show that the proposed image recognition method has achieved excellent performance on tourist images, and the evaluation indexes such as accuracy and recall are better than the existing algorithms. At the same time, the recommendation system also shows good personalized recommendation ability, which improves the accuracy by about 10% and the user satisfaction by about 15%. This research provides a complete set of intelligent solutions for tourism, which is expected to inject new vitality into the transformation and upgrading of tourism.