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.

Tourist attraction and tour route recommendation are the key research highlights in the field of smart tourism. Currently, the existing recommendation algorithms encounter certain problems when making decisions regarding tourist attractions and tour routes. This paper presents a smart tourism recommendation algorithm based on a cellular geospatial clustering and weighted collaborative filtering. The problems are analyzed and concluded, and then the research ideas and methods to solve the problems are introduced. Aimed at solving the problems, the tourist attraction recommendation model is set up based on a cellular geographic space generating model and a weighted collaborative filtering model. According to the matching degree between the tourists’ interest needs and tourist attraction feature attributes, a precise tourist attraction recommendation is obtained. In combination with the geospatial attributes of the tourist destination, the spatial adjacency clustering model based on the cellular space generating algorithm is set up, and then the weighted model is introduced for the collaborative filtering recommendation algorithm, which ensures that the recommendation result precisely matches the tourists’ needs. Providing precise results, the optimal tour route recommendation model based on the precise tourist attraction approach vector algorithm is set up. The approach vector algorithm is used to search the optimal route between two POIs under the condition of multivariate traffic modes to provide the tourists with the best motive benefits. To verify the feasibility and advantages of the algorithm, this paper designs a sample experiment and analyzes the resulting data to obtain the relevant conclusion.

Aimed at current problems in tour route planning, this research proposes a tour route planning algorithm based on the data mining of tourist sights based on their precise interest. It further studies on the aspect of tourist interests and needs, tourist sight feature attributes, data mining of tourist sights based on their precise interest and optimal tour route planning, and then designs and performs experiment to testify the feasibility and superiority of the algorithm. First, tourist interest and need label matrix and tourist sight feature attribute matrix are set up. Tourist sight attribute factor is formed by mining text encyclopedic data, and combining with other factors, tourist sight clustering algorithm is set up to output tourist sight clusters. Second, data mining algorithm of tourist sights based on their precise interest is set up, which outputs the optimal tourist sights not only match the tourists’ interests and needs but also optimally distribute around to produce the lowest travel costs. Third, based on precise interested tourist sights, combining with geographic information data, traffic information data and tourist sight information data, optimal tour route planning algorithm based on interest label is set up. It outputs maximum heap and complete binary tree with descending order interval motive values and corresponding edge clipping circle, which directly reflects the satisfaction content of each tour route on tourists. Tourist sight domain and geographic spatial data of Zhengzhou city are collected as basic data to design the experiment, and it testifies the algorithm’s feasibility, effectiveness and practicalness. Meanwhile, the experiment brings in three commonly used route planning algorithms as control group to make comparison with the developed algorithm on the aspect of optimal route, motive value, motive value difference, algorithm performance and guide map, etc., which testifies that the developed algorithm has superiority to traditional algorithms. The research indicates that this developed algorithm solves the problems on tour route planning and it is an effective, feasible and practical algorithm.

The route planning before tourists going to an unfamiliar city is relatively subjective, which can hardly provide maximum motive benefits. Aimed at the problem, tourism route decision support based on neural net buffer analysis model is brought up in this paper. Firstly, sight spots in Zhengzhou city are taken as examples to ensure research objects. GIS buffer analysis and manual neural net are taken as basic model to ensure research method, on which base, motive iterative function transmission model related to neural net transmission chain and service facility in sight spots buffer is set up. And then three downtown sight spots are taken as algorithm example to research the feasibility of the model to calculate motive iterative function output value of different tourism routes with the same sight spots, which provides objective tourism route decision support for tourists. Algorithm example proves that the model brought up in this paper is feasible and valuable, and can provide effective decision support for tourists.