Urban social civilization and the quality of life of residents are gradually improved, and the development scale and trend of the leisure tourism industry have been growing. This paper constructs a multi-source data fusion model based on an ensemble learning algorithm, uses Ctrip 2020 open data set to train the model, and then obtains the tourism information data processing and prediction results. This paper takes the data of Ctrip as the training set and compares the trained model with the data of tunic and Feizhu. In this paper, sensor detection technology is used to analyze many famous scenic spots in China, including tourist type, gender, and location. The results show that tourism feature extraction results are consistent with data from trending flying bamboo, tunics, and other websites, according to the results of a multi-source fusion of tourism information. Among them, in the data of the first half of 2020, the prediction accuracy of the model after data processing is about 62%. Affected by the epidemic situation, the accuracy of the model is low. In the second half of the year, the prediction accuracy is 78%, which can be used to fuse tourism information in a short time. Therefore, the data show that the model has high learning ability and high trend prediction ability in tourism data processing, which can provide necessary information support for tourists.
Inverse Source Data-Processing Strategies for Radio-Frequency Localization in Indoor Environments