A smart tour route planning algorithm based on a Naïve Bayes interest data mining machine learning is brought forward in the paper, according to the problems of current tour route planning methods. A machine learning model of Naïve Bayes interest data mining is set up by learning a mass of training data on tourists’ interests and needs. Through the recommended interest tourist site classifications from the machine learning module, the optimal tourist site mining algorithm based on the membership degree searching propagating tree of a tourist’s temporary accommodation is set up, which mines and outputs the optimal tourist sites. The mined optimal tourist sites are taken as seed points to set up a tour route planning algorithm based on the optimal propagating tree of a closed-loop structure. Through the proposed algorithm, an experiment is designed and performed to output optimal tour routes conforming to tourists’ needs and interests, including the propagating tree closed-loop structures, a minimum heap of propagating tree weight function value, and a weight function value complete binary tree. We prove that the proposed algorithm has the features of intelligence and accuracy, and it can learn tourists’ needs and interests to output optimal tourist sites and tour routes and ensure that tourists can get the best motive benefits and travel experience in the tour process, by analyzing the experiment data and results.

Currently, the recommendation method on tourist sight and tour route lacks of the mechanism of tourists’ interests mining and the precise tourist sights recommending, and the planned tour routes cannot properly and adequately combine with the real world environment. Meanwhile, the research on the multivariate transportation modes in tour route recommendation is not sufficient. Aim at the problems of the tourist sight and tour route recommendation in intelligent tourism recommendation system of the intelligent tourism construction, this paper brings forward a tourism recommendation algorithm based on text mining and MP nerve cell model of multivariate transportation modes. The research specially focuses on the optimal tourist sight matching algorithm based on tourists’ interests mining and the optimal tour route chain algorithm based on the multivariate transportation modes. First, it analyzes the problems on tourism recommendation, based on which, the tourist sight clustering algorithm on feature attribute label and the tourist sight text mining algorithm on interest label are developed. The mined tourist sights will approach tourists’ interests to the maximum extent. Secondly, Considering the critical impact of the selected transportation mode on motive benefit satisfaction in the tour route chain, the tour route chain algorithm based on the nerve cell model of multivariate transportation modes is developed. This algorithm combines with geographic information element and transportation element, and it simulates the bionic principle of input and output information process on MP nerve cell, then the tour route chain model based on the nerve cell of multivariate transportation modes is set up. Through the iteration of multiple layer nerve cell motive weight values and accommodation coefficients, the algorithm finally outputs the signal information flow motive values, in which the tour route chain with the maximum information flow motive value is generated. Thirdly, to testify the feasibility and practicalness of the algorithm, an experimental example in real-world environment is designed and performed. The feasible matched tourist sights and tour route chains are output, meanwhile, the three commonly used optimal route searching algorithms are set as the control group, and along with the developed algorithm, they are compared with each other on the aspect of optimal tour route chain. The experiment testifies that the developed algorithm is feasible and practical, and has advantages on the tourism recommendation. Through the algorithm design and the experiment, it finds that the mined tourist sights by the objective function in the algorithm can best match tourists’ interest labels. The algorithm adequately combines with the real world tourism data of the geographic information, traffic information and tourist sight information and outputs the tour routes that best match tourists’ interests. Compare with the control group algorithms, the tour routes output by the developed algorithm have the highest motive satisfaction, lowest time complexity and space complexity. The developed algorithm is mainly used as the embedded algorithm for the intelligent tourism recommendation system, whose direct aim is to provide service for the tourists. Meanwhile, it can also provide service for the tourism administrations to collect, manage and mine the interest data as well as discover knowledge, and help the government to optimize the urban transportation system, launch the public vehicles and optimize the transportation guarantee strategy.

Federated analytics (FA) over spatial data with local differential privacy (LDP) has attracted considerable research attention recently. Existing solutions for this problem mostly employ a uniform grid (UG) structure, which recursively decomposes the whole spatial domain into fine‐grained regions in the distributed setting. In each round, the sampled clients perturb their locations using a random response mechanism with a fixed probability. This approach, however, cannot encode the client’s location effectively and will lead to ill‐suited query results. To address the deficiency of existing solutions, we propose LDP‐FSRQ, a spatial range query algorithm that relies on a hybrid spatial structure composed of the UG and quad‐tree with nonuniform perturbation (NUP) probability to encode and perturb clients’ locations. In each iteration of LDP‐FSRQ, each client adopts the quad‐tree to encode his/her location into a binary string and uses four local perturbation mechanisms to protect the encoded string. Then, the collector prunes the quad‐tree of the current round according to the clients’ reports and shares the pruned tree with the clients of the next round. We demonstrate the application of LDP‐FSRQ on Beijing, Landmark, Check‐in, and NYC datasets, and the experimental results show that our approach outperforms its competitors in terms of queries’ utility.