# SM-PageRank Algorithm-Based User Interest Model for Mobile Smart Tourism Platform

Authors: Hua Li; Tao Su
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

Smart tourism, also known as smart tourism, actively captures tourism activities, tourists, tourism economy, tourism resources, and other information through mobile Internet and mobile terminal Internet of things devices and emerging technologies such as cloud computing and Internet of things. In order to release the intelligent tourism information in time, let the masses know the information in time, and adjust the work and tourism plan in time, this paper proposes SM-PageRank algorithm and secondary ranking based on user interest model, in order to study the accuracy of tourism information retrieval. The methods used in this paper include the principle of three weighted information fusion algorithms, LBS technology, and the design of intelligent tourism system. The function of information fusion algorithm is to find the global optimal solution for travel routing. LBS technology collects real-time tourism information through some entity sensors. Through information retrieval experiment and fusion technology solution experiment, the results show that the SM-PageRank algorithm and the secondary sorting based on user interest model proposed in this paper improve the average accuracy by 20.1% compared with the traditional algorithm and 2.6% compared with Google search. The Internet of things fusion algorithm gives a line planning set with standard deviation of 0.4 for the set of travel days with standard deviation of 1.92.


---

## Body

## 1. Introduction

Tourism has become one of the leisure activities of most people. Users usually retrieve tourism information on the search platform when planning their trip. However, the amount of tourism information stored on the Internet is becoming more and more complex, and users pay more and more attention to the relevance of tourism information provided by the search platform. After users input search words on the search platform, they should always match the search words and select the most relevant and reliable tourism information at the top of the search results. Providing users with the most relevant and reliable information source as search results and allowing them to really enjoy smart tourism is one of the urgent problems to be solved by the search platform. Therefore, one of the main research directions of search engine is search sorting algorithm. With the increase in the number of web pages, manual classification cannot keep up with the pace of the times, search engines have evolved to text retrieval, and SM-PageRank sorting algorithm came into being. This algorithm has a very good effect on ranking web pages, so it is often used to solve the problem of intelligent tourism systems.


### 1.1. Background

With the rapid development of communication technology and microelectronics, the traditional computer-centered computing mode has gradually changed into a human-centered general computing mode. Therefore, the concept of the Internet of things is gradually being proposed. This technology allows people to access the Internet of things at any time and on demand through mobile phones, handheld computers, computers, and other devices, receive relevant information, and accept the surrounding environment on demand. Therefore, Intelligent Tourism search engine will become one of the main trends in the future. In the Intelligent Tourism retrieval platform, the quality of retrieval algorithm will directly affect the efficiency and accuracy of search results.


### 1.2. Significance

When users use the search engine to obtain tourism information, the search engine cannot find the important information users need and put it in the first place, which greatly affects users’ satisfaction with the search engine. Therefore, how to design a search ranking algorithm suitable for search engine is particularly important. Next, this paper studies the problem of smart tourism route planning. Based on the problem of self-driving travel route planning, a mathematical model of intelligent travel route planning is established. The result of the optimal solution is transformed into a regional smart tourism route plan, and finally, a smart tourism route plan is formed. The tourism route arrangement given in this paper has strong practical guiding significance and can provide a scientific basis for the actual travel arrangement.


### 1.3. Innovation Points

This paper uses SM-PageRank sorting algorithm and secondary sorting algorithm, summarizes the previous research experience, and proposes to apply the similarity between the calculated page and its linked page to the calculation of PageRank value, so as to ensure the accuracy of search query. This article applies the Internet of things technology to tourism route planning, so that the average travel time of each scenic spot can be well controlled and has high stability.


## 1.1. Background

With the rapid development of communication technology and microelectronics, the traditional computer-centered computing mode has gradually changed into a human-centered general computing mode. Therefore, the concept of the Internet of things is gradually being proposed. This technology allows people to access the Internet of things at any time and on demand through mobile phones, handheld computers, computers, and other devices, receive relevant information, and accept the surrounding environment on demand. Therefore, Intelligent Tourism search engine will become one of the main trends in the future. In the Intelligent Tourism retrieval platform, the quality of retrieval algorithm will directly affect the efficiency and accuracy of search results.


## 1.2. Significance

When users use the search engine to obtain tourism information, the search engine cannot find the important information users need and put it in the first place, which greatly affects users’ satisfaction with the search engine. Therefore, how to design a search ranking algorithm suitable for search engine is particularly important. Next, this paper studies the problem of smart tourism route planning. Based on the problem of self-driving travel route planning, a mathematical model of intelligent travel route planning is established. The result of the optimal solution is transformed into a regional smart tourism route plan, and finally, a smart tourism route plan is formed. The tourism route arrangement given in this paper has strong practical guiding significance and can provide a scientific basis for the actual travel arrangement.


## 1.3. Innovation Points

This paper uses SM-PageRank sorting algorithm and secondary sorting algorithm, summarizes the previous research experience, and proposes to apply the similarity between the calculated page and its linked page to the calculation of PageRank value, so as to ensure the accuracy of search query. This article applies the Internet of things technology to tourism route planning, so that the average travel time of each scenic spot can be well controlled and has high stability.


## 2. Related Work

With the rapid development of information technology and the increasingly prominent role of tourism in the national economy, the concept of smart tourism is becoming more and more popular, which has been studied by many scholars. Predecessors’ research mostly focused on professional training of tour guides, and there were few applications of the Internet of things. Chen et al. believe that it is necessary to manage the coherent quality. Whenever the component system used for reinforcement learning involves randomness and continuous control behavior of reliability and quality, they often have problems such as failure and error. The complexity and stochastic characteristics of challenge the operation of the system. They also put forward a dynamic quality calculation method. It focuses on the construction of axiomatic framework, but their method sacrifices accuracy for coherence, resulting in excessive error [1]. Fang et al. believe that data transmission is very important for analysis and learning in the Tourism Internet of things system, and transmitting data with limited energy is a challenge. They studied the data transmission in the energy collection system using capacitors to provide energy when the energy reception rate changes with time. They proposed that the more energy the capacitors receive, the slower the energy reception speed. Based on this feature, they studied how to transmit more data when the energy receiving time is discontinuous, but their proposed method needs to invest a lot of cost in infrastructure construction [2]. Alberti et al. believe that the Internet of things has been actively challenging the current Internet system. The Internet has many architectural limitations, such as security, data distribution efficiency, source, and traceability, so the Internet of things with good security has more development prospects. Their research aims to produce a prototype of indoor security monitoring system with fingerprint identification, using the Internet network for the data transmission process on the network database, the system constructs a complete ecosystem and has reference significance for outdoor tourism, but the timeliness of the system is not strong enough and the information lag is obvious [3]. Safii et al. believe that as a provider of data services, servers play a very important role for organizations with huge data use and control systems. If the temperature is too high and the operator does not notice it, the hardware equipment in the server room may be damaged. To solve this problem, they built a temperature monitoring prototype using multiple IOT temperature sensors and web-based responsive software, so as to easily view the temperature on various PC and smartphone displays to ensure the safe operation of the server. However, it also needs real-time monitoring of humidity, surrounding environment, and other information [4]. Sang et al. explore the national image of using smart tourism applications during large-scale event visits through perceived value. Their research aims to test the impact of the image of tourist areas on the country. They collected and analyzed 122 survey responses from foreign tourists for the 2018 Pingchang Olympic Games, emphasizing the importance of participating in large-scale events and how tourist attractions affect the overall national image in the eyes of tourists, but their research does not deeply understand the importance of managing large tourist attractions [5]. Li et al. believe that although smart tourism has received considerable research attention, few studies have investigated the determinants of how tourists evaluate their service experience. They try to explore how commercial value propositions shape the functional and emotional results of travel experience and influence the evaluation of service experience. After research, it is concluded that the novelty of tourism has a positive impact on tourists’ consumption. Perceived enjoyment, perceived enjoyment, and perceived usefulness all have a positive impact on the evaluation of service experience. But they focus on the evaluation of smart tourism and have less research on construction [6]. Sun first discussed the research significance and research status of wireless sensor networks at home and abroad, analyzed and optimized the sensor network algorithm, and designed and developed a tourism management system based on the existing wireless sensor network hardware experimental system. However, his system is still a travel agency model, but the travel agency business is expanded to the network [7]. Ashari studied the development track of Bandung tourism and proposed that the limitation of its development is the ability of tourists to obtain information about Bandung natural tourism. When global tourists are confused about looking for new and popular tourist attractions, they will not think of Bandung. Through smart tourism based on service architecture, emphasizing new social methods and implementing network physical social system, it can attract a large number of tourists to Bandung. However, the smart tourism model he proposed depends on the operating system, requires database storage, occupies storage space, and charges users [8]. According to the field observation and microstructure study of the larger regional shear deformation zone and the scale shear deformation zone of the mining area, it is found that the shear deformation has similar characteristics at different scales from the macro to the micro and constitutes a discriminating shear deformation zone. The existing geological basis also provides a guarantee for further understanding of the relationship between geological mineralization and shear zones.


## 3. Internet of Things Integration Technology Applied to Smart Tourism

### 3.1. Principle of Three Weighted Information Fusion Algorithms

Due to the universality of the Internet of things, a large number of multisource and multitype data are collected and stored [9]. Therefore, the massive data of the Internet of things is not only reflected in its growing amount of data over time but also reflected in the high-attribute dimension characteristics of multisource and multitype devices that perceive objective things from multiple angles. If the Internet of things data features with high attribute dimension cannot be effectively fused, it will bring great computational challenges for further decision-making level fusion [10].
(1)
Weighted average fusion algorithmThe weighted average fusion algorithm usually uses the mathematical average formula, that is, the weight of each IOT sensor is the same, referred to as NVFA [11]. In the tth sensor fusion system, sensors T1,T2,⋯Tt estimate the state of the same target, and the measured values of each sensor are x1,X2⋯XT, which are independent of each other. The true value to be estimated is represented by X, and its variance is Q12,Q22⋯Qt2. The measured value of the pth sensor is represented by Xpi. Suppose the weight of each sensor is W1,W2⋯WT, and the fusion state estimation and weight meet the following conditions:
(1)x^=∑i=1twixi,∑i=1twi=1.Assume that the equal weight value isw. As can be seen from equation (1), the state estimation after local fusion is as follows:
(2)x^=∑i=1twixi=1t∑i=1txi.The total mean square error is(3)Q2=Ex−x^2=E∑i=1twix−xi2=E∑i=1twi2x−xi2+2∑i=1twix−xiwtx−xt.BecauseXiI=1,2,⋯T is an unbiased estimate of X and is not related to each other, the following formula is obtained:
(4)Ex−xix−xt=0i,t=1,2,..n,i≠t.Therefore, the total mean square error is(5)Q2=E∑i=1twi2x−xi2=∑i=1twi2Qi2.From equations (1) and (5), for the weighted average algorithm, the sum of mean square errors is
(6)Q2=∑i=1tQi2t2.(2)
Optimal weighting algorithmAccording to formula (5), the mean square error is a multivariate quadratic function of the weight factor, so a minimum value is required. The best weighting is to multiply the measured value of each IOT sensor by the weighting factor of the measured data x1,X2,⋯XT according to a specific standard to maximize the sensor measurement data and reduce the loss of information caused by intermediaries other than measurement, which is abbreviated as OVFA [12]. According to the Lagrange conditional extreme value method, the auxiliary function is constructed as follows:
(7)fw1,w2,⋯,wt,θ=∑i=1twi2Qi2−θ∑i=1twi−1.Establish equations:(8)∂f∂wi=2wiQi2−θ=0i=1,2⋯t,∂f∂θ=1−∑i=1twi=0.Accordingly, the weighting factor with the minimum total mean square error is obtained:(9)wi=Qi−2∑i=1tQi−2.The total mean square error is(10)Q2=Ex−x^2=E∑p=1twpx−x^2+2∑p=1twpx−xpwqx−xq.Becausex1,X2,⋯XT are independent of each other and are unbiased estimates of X, when p≠q, it can be obtained Ex−xpx−xq=0. Therefore, Q2 can be written as follows:
(11)Q2=E∑p=1twpx−xp2=∑p=1twp2Qp2.The above formula can be obtained in combination with formula (9), and the total mean square error is as follows:
(12)Q2=1∑i=1tQi−2.(3)
Adaptive weighted data fusionThe weighting factor satisfies equation (9). Here, the formula for calculating the sensor variance is directly given, and the detailed derivation process is described in the literature. On the basis of optimized weighting, adaptive weighted data fusion fully considers the historical data of each sensor to obtain the data fusion value with the minimum mean square error. The abbreviation of this algorithm is AVFA [13]. The formula for calculating the sensor variance is
(13)Qp2=Epp−Eqp.Of which,(14)Epp=1i∑i=1txpi,Eqp=1t−1∑q=1ti−1iEqpi−1+1txpixqi.The mean value of sensorP at time I is as follows:
(15)xp¯i=1i∑i=1txpi,p=1,2⋯n.The estimated value at this time is as follows:(16)x¯=∑p=1twpxp¯i.The total mean square error is as follows:(17)Q2=Ex−xp¯i2=E∑p=1twpx−xp¯i2+2∑p=1twpwqx−xpix−xqi.Becausex1,X2,⋯XT are independent of each other and are unbiased estimates of X, when p≠q, it can be obtained Ex−xpx−xq=0. Therefore, Q2 can be written as the following [14]:
(18)Q2=E∑p=1twpx−xp¯i2=1i∑p=1twp2Qp2.The corresponding total mean square error is as follows:(19)Q2=1i∑i=1tQi−2.It can be seen from the above analysis that the weighted average fusion algorithm selects the mathematical average formula, and the weight of each IOT sensor is approximately equal [15]. This method is difficult to overcome the randomness of shorthand measurement and the fluctuation of measured values caused by some sensor faults. On the basis of weighted average, the weighted fusion algorithm fully considers the influence of each sensor on the measured data when some sensors cannot have good strain capacity [16]. However, in the calculation of weighted estimation and total variance, only the measurement data of the current sensor are used, and the measurement data of the historical time of the sensor are ignored. The adaptive weighted fusion algorithm fully considers the historical data of each sensor. On the basis of optimal weighting, the data fusion value with the minimum mean square error is taken, but the estimated value and weighting factor of each time are not effectively processed [17]. In view of the above shortcomings, this section proposes an information fusion method based on the combination of knife cutting method and adaptive weighting method and performs Quenouille estimation on the weighting coefficient and estimated value, respectively, so as to improve the accuracy and robustness of data processing.


### 3.2. LBS Technology and Smart Tourism

LBS, also known as location-based service, is a value-added service that provides services corresponding to users. The method is to obtain the location information of mobile terminal users through the wireless communication network of mobile communication operators and external positioning methods [18]. Travel behavior has high mobility and can be well combined with location-based services. The provision of geographic positioning services can only be achieved through the positioning technology of the corresponding geographic information system platform, such as base station positioning, landmark positioning, wireless network positioning, RFID, and GPS positioning [19]. With the application of GPS modules and with the increasing popularity of smart phones, travel applications on mobile devices such as smart phones have become the best carrier for LBS applications. With the rapid popularization of smart terminals and mobile Internet applications, as well as the popularization of electronic maps and navigation software, the demand for creative services based on public services such as personal location services, Internet maps, and electronic navigation has grown rapidly, and people have become the subject of LBS. LBS integrates electronic maps, location services, and travel services to provide location services, allowing users to experience a brand new service experience brought by smart tourism [20].Location-based service is to obtain the location of current user information through the cooperation of mobile terminal, wireless network, and other terminal devices and provide personalized location information service according to the needs of users. Positioning service requires two processes: one is the positioning process, which not only obtains the latitude, longitude, and altitude of the current location of the user’s mobile phone but also not directly provides usually this information to the user [21]. The second is location service, which not only obtains information through location operation but also realizes some user-oriented services [22].


### 3.3. Smart Tourism System Design

The system designed in this paper is based on LBS technology, MVC mode, and SSH open source framework technology. The system architecture design adopts a hierarchical structure, which is composed of four different layers: business application layer, data layer, service layer, and operation support layer [23]. The system design is shown in Figure 1.Figure 1
Design diagram of smart tourism system.The operation support layer is the carrier of the smart tourism service platform. It adopts advanced software and hardware equipment to build an operating system suitable for stable operation, including network, server, and security equipment. As the software environment of computer operating system and data platform software, the data layer is the content of management related database provided to the application layer through the service layer [24]. The data mainly includes librarian data and tourism service data. The service layer mainly provides various application services to the application layer through the online travel service system, such as API interface service of WeChat public platform, SDK of WeChat public platform, data collection service, and data query service [25]. The business application layer provides various services and business functions for the system service objects (tourists).The overall structure of tourism flow management consists of three main parts: decision-making, command and coordination control, and data analysis and processing and data acquisition and transmission [26]. As shown in Figure 2.Figure 2
Overall framework of tourist management.


## 3.1. Principle of Three Weighted Information Fusion Algorithms

Due to the universality of the Internet of things, a large number of multisource and multitype data are collected and stored [9]. Therefore, the massive data of the Internet of things is not only reflected in its growing amount of data over time but also reflected in the high-attribute dimension characteristics of multisource and multitype devices that perceive objective things from multiple angles. If the Internet of things data features with high attribute dimension cannot be effectively fused, it will bring great computational challenges for further decision-making level fusion [10].
(1)
Weighted average fusion algorithmThe weighted average fusion algorithm usually uses the mathematical average formula, that is, the weight of each IOT sensor is the same, referred to as NVFA [11]. In the tth sensor fusion system, sensors T1,T2,⋯Tt estimate the state of the same target, and the measured values of each sensor are x1,X2⋯XT, which are independent of each other. The true value to be estimated is represented by X, and its variance is Q12,Q22⋯Qt2. The measured value of the pth sensor is represented by Xpi. Suppose the weight of each sensor is W1,W2⋯WT, and the fusion state estimation and weight meet the following conditions:
(1)x^=∑i=1twixi,∑i=1twi=1.Assume that the equal weight value isw. As can be seen from equation (1), the state estimation after local fusion is as follows:
(2)x^=∑i=1twixi=1t∑i=1txi.The total mean square error is(3)Q2=Ex−x^2=E∑i=1twix−xi2=E∑i=1twi2x−xi2+2∑i=1twix−xiwtx−xt.BecauseXiI=1,2,⋯T is an unbiased estimate of X and is not related to each other, the following formula is obtained:
(4)Ex−xix−xt=0i,t=1,2,..n,i≠t.Therefore, the total mean square error is(5)Q2=E∑i=1twi2x−xi2=∑i=1twi2Qi2.From equations (1) and (5), for the weighted average algorithm, the sum of mean square errors is
(6)Q2=∑i=1tQi2t2.(2)
Optimal weighting algorithmAccording to formula (5), the mean square error is a multivariate quadratic function of the weight factor, so a minimum value is required. The best weighting is to multiply the measured value of each IOT sensor by the weighting factor of the measured data x1,X2,⋯XT according to a specific standard to maximize the sensor measurement data and reduce the loss of information caused by intermediaries other than measurement, which is abbreviated as OVFA [12]. According to the Lagrange conditional extreme value method, the auxiliary function is constructed as follows:
(7)fw1,w2,⋯,wt,θ=∑i=1twi2Qi2−θ∑i=1twi−1.Establish equations:(8)∂f∂wi=2wiQi2−θ=0i=1,2⋯t,∂f∂θ=1−∑i=1twi=0.Accordingly, the weighting factor with the minimum total mean square error is obtained:(9)wi=Qi−2∑i=1tQi−2.The total mean square error is(10)Q2=Ex−x^2=E∑p=1twpx−x^2+2∑p=1twpx−xpwqx−xq.Becausex1,X2,⋯XT are independent of each other and are unbiased estimates of X, when p≠q, it can be obtained Ex−xpx−xq=0. Therefore, Q2 can be written as follows:
(11)Q2=E∑p=1twpx−xp2=∑p=1twp2Qp2.The above formula can be obtained in combination with formula (9), and the total mean square error is as follows:
(12)Q2=1∑i=1tQi−2.(3)
Adaptive weighted data fusionThe weighting factor satisfies equation (9). Here, the formula for calculating the sensor variance is directly given, and the detailed derivation process is described in the literature. On the basis of optimized weighting, adaptive weighted data fusion fully considers the historical data of each sensor to obtain the data fusion value with the minimum mean square error. The abbreviation of this algorithm is AVFA [13]. The formula for calculating the sensor variance is
(13)Qp2=Epp−Eqp.Of which,(14)Epp=1i∑i=1txpi,Eqp=1t−1∑q=1ti−1iEqpi−1+1txpixqi.The mean value of sensorP at time I is as follows:
(15)xp¯i=1i∑i=1txpi,p=1,2⋯n.The estimated value at this time is as follows:(16)x¯=∑p=1twpxp¯i.The total mean square error is as follows:(17)Q2=Ex−xp¯i2=E∑p=1twpx−xp¯i2+2∑p=1twpwqx−xpix−xqi.Becausex1,X2,⋯XT are independent of each other and are unbiased estimates of X, when p≠q, it can be obtained Ex−xpx−xq=0. Therefore, Q2 can be written as the following [14]:
(18)Q2=E∑p=1twpx−xp¯i2=1i∑p=1twp2Qp2.The corresponding total mean square error is as follows:(19)Q2=1i∑i=1tQi−2.It can be seen from the above analysis that the weighted average fusion algorithm selects the mathematical average formula, and the weight of each IOT sensor is approximately equal [15]. This method is difficult to overcome the randomness of shorthand measurement and the fluctuation of measured values caused by some sensor faults. On the basis of weighted average, the weighted fusion algorithm fully considers the influence of each sensor on the measured data when some sensors cannot have good strain capacity [16]. However, in the calculation of weighted estimation and total variance, only the measurement data of the current sensor are used, and the measurement data of the historical time of the sensor are ignored. The adaptive weighted fusion algorithm fully considers the historical data of each sensor. On the basis of optimal weighting, the data fusion value with the minimum mean square error is taken, but the estimated value and weighting factor of each time are not effectively processed [17]. In view of the above shortcomings, this section proposes an information fusion method based on the combination of knife cutting method and adaptive weighting method and performs Quenouille estimation on the weighting coefficient and estimated value, respectively, so as to improve the accuracy and robustness of data processing.


## 3.2. LBS Technology and Smart Tourism

LBS, also known as location-based service, is a value-added service that provides services corresponding to users. The method is to obtain the location information of mobile terminal users through the wireless communication network of mobile communication operators and external positioning methods [18]. Travel behavior has high mobility and can be well combined with location-based services. The provision of geographic positioning services can only be achieved through the positioning technology of the corresponding geographic information system platform, such as base station positioning, landmark positioning, wireless network positioning, RFID, and GPS positioning [19]. With the application of GPS modules and with the increasing popularity of smart phones, travel applications on mobile devices such as smart phones have become the best carrier for LBS applications. With the rapid popularization of smart terminals and mobile Internet applications, as well as the popularization of electronic maps and navigation software, the demand for creative services based on public services such as personal location services, Internet maps, and electronic navigation has grown rapidly, and people have become the subject of LBS. LBS integrates electronic maps, location services, and travel services to provide location services, allowing users to experience a brand new service experience brought by smart tourism [20].Location-based service is to obtain the location of current user information through the cooperation of mobile terminal, wireless network, and other terminal devices and provide personalized location information service according to the needs of users. Positioning service requires two processes: one is the positioning process, which not only obtains the latitude, longitude, and altitude of the current location of the user’s mobile phone but also not directly provides usually this information to the user [21]. The second is location service, which not only obtains information through location operation but also realizes some user-oriented services [22].


## 3.3. Smart Tourism System Design

The system designed in this paper is based on LBS technology, MVC mode, and SSH open source framework technology. The system architecture design adopts a hierarchical structure, which is composed of four different layers: business application layer, data layer, service layer, and operation support layer [23]. The system design is shown in Figure 1.Figure 1
Design diagram of smart tourism system.The operation support layer is the carrier of the smart tourism service platform. It adopts advanced software and hardware equipment to build an operating system suitable for stable operation, including network, server, and security equipment. As the software environment of computer operating system and data platform software, the data layer is the content of management related database provided to the application layer through the service layer [24]. The data mainly includes librarian data and tourism service data. The service layer mainly provides various application services to the application layer through the online travel service system, such as API interface service of WeChat public platform, SDK of WeChat public platform, data collection service, and data query service [25]. The business application layer provides various services and business functions for the system service objects (tourists).The overall structure of tourism flow management consists of three main parts: decision-making, command and coordination control, and data analysis and processing and data acquisition and transmission [26]. As shown in Figure 2.Figure 2
Overall framework of tourist management.


## 4. Tourism Information Algorithm Design and Teaching Management Experiment

### 4.1. Construction of Experimental Platform

This experiment will use Solr and Nutch to build an information retrieval experiment platform, capture experimental data through Nutch, modify Nutch’s existing scoring algorithm, and apply the SM-P algorithm to realize the user interest module [27]. This paper uses Nutch, Solr, and other tools to build the Intelligent Tourism search experimental platform, and its overall structure is shown in Figure 3.Figure 3
Overall platform architecture.Nutch is a Lucene-based search engine system. It not only provides all the tools required by the search engine but also can crawl billions of web pages every month and index these crawled web pages. However, in order to meet the distributed requirements, Nutch has gradually become a crawler tool, and the retrieval function has been given to other tools. Solr is an enterprise search engine server that can index the retrieved data sources. Solr provides a search interface for application search. Like Lucene, Solr provides full-text search, keyword highlighting, and management interfaces. Configuring the Chinese word segmentation method is a necessary step before indexing with Solr [28]. In this experiment, the IK analyzer word segmentation tool is used to finally configure the word segmentation effect.In this experiment, five search words (mountain climbing, travel agency, food, scenic spots, and outbound tourism) were selected for query. When comparing the results, the first 50 items of the result set returned by each search word were taken as the investigation objects. The comparison of query statistics and accuracy is shown in Table1.Table 1
Statistics of search results of traditional PageRank and SM-PageRank algorithms.
Key wordsPageRank algorithmSM-PageRank algorithmSearch resultsSelectionRelated webpagesAccuracySearch resultsSelectionRelated webpagesAccuracyClimb mountains258503876%256504386%Travel agency279512956.9%257515098%Delicacy208523363.5%324524994.2%Scenic spots206533158.5%296534075.5%Outbound tourism205543870.4%329543972.2%Six testers were interviewed to search the above keywords and score the satisfaction of the search results. The comparison of accuracy and satisfaction of the two algorithms is shown in Figure4.Figure 4
Comparison of accuracy and satisfaction of PageRank and SM-PageRank.According to the above figure and table, the optimized SM-PageRank algorithm is better than the traditional PageRank algorithm.Then, five search words (mountain climbing, travel agency, food, scenic spots, and outbound tourism) are also selected for query, and the correlation between the experimental search results and the query content is manually compared through SM-PageRank algorithm and Google search engine [29]. In the query results, the platform takes the top 50 returned results list as the research basis, and the top 100 Google search results are analyzed and compared. The query statistics are shown in Table 2.Table 2
Sorting comparison of query results.
Key wordsSM-PageRank algorithmGoogle searchSearch resultsSelectionRelated webpagesAccuracySearch resultsSelectionRelated webpagesAccuracyClimb mountains258503876%123061004343%Travel agency279512956.9%118551005656%Delicacy208523363.5%155521007676%Scenic spots206533158.5%129941007070%Outbound tourism205543870.4%135891006767%Because the experimental platform takes a short time to grab web pages and the number of web pages is less than that of Google search engine, the total number of returned results is also less, which is not as good as Google search engine in recall.To establish a secondary sorting algorithm, the experiment needs to establish a user interest model for users. This experiment establishes a user’s interest model through user registration. The contents required to be filled in by the user during registration (such as route, diet, accommodation, comments, etc.) are of interest to the user. After the user registration is completed, the results of secondary sorting search are given to establish a user interest model for the user. The results returned by secondary sorting are more in line with user needs. Figure5 shows the comparison of SM-PageRank and Google search accuracy.Figure 5
Comparison of SM-PageRank and secondary sorting accuracy and satisfaction.By comparing the above experimental results, we conclude that the search accuracy of SM-PageRank algorithm is higher than that of conventional PageRank algorithm, and the secondary ranking based on user interest model makes the search results more appropriate. As can be seen from the above figure, the search results of SM-PageRank can better meet the needs of users.After connecting the major scenic spots, this algorithm can also count the passenger flow, including real-time passenger flow statistics and attribute distribution statistics. Figure6 shows the relationship between passenger flow and temperature according to system statistics.Figure 6
Passenger flow statistics interface.


### 4.2. Fusion Technology for Solving Travel Problems

Traditional tourist routes are usually one or more fixed routes designed by travel agencies, which are suitable for fixed routes and group tourism. Traditional tourism routes are the crystallization of a tourism experience, which usually takes a certain time to design [30]. Artificial intelligence has greatly improved the rationality of personal travel routes. The integration of Internet of things into route planning has greatly promoted the intelligence of travel route planning. It is more scientific, persuasive, and efficient to solve the problem of tourism route planning by establishing a model and integrating the Internet of things.Internet of things fusion is one of the effective algorithms to solve the problem of travel route planning. In order to apply Internet of things fusion to solve the problem of tourism route planning, it is necessary to improve the Internet of things fusion, so that the improved Internet of things fusion can obtain the optimal solution with higher probability in the shortest time. Internet of things integration to solve the traveling salesman problem mainly depends on the path selection strategy, pheromone update rules, and the setting of relevant parameters, and the improvement of Internet of things integration mainly focuses on these three aspects. The improvement of path selection strategy mainly aims at the defect that Internet of things fusion is easy to fall into local optimal solution. By improving the path selection probability, local search and other strategies, it is possible to help the Internet of things fusion jump out of the local optimal solution, so as to improve the quality of the solution. Pheromone accumulation and feedback are also the improvement focus of pheromone update rules, which is a better method to guide pheromones to the best path and avoid the influence of pheromones on sensors on poor paths. For the setting of relevant parameters, different traveling salesman problems can be solved more effectively. At present, there is no very good mathematical proof to help judge the quality of parameters. Generally speaking, the range of parameters is determined by experience, and then, the size of parameters is dynamically adjusted.The optimal solution path map and local optimal solution path map of Internet of things fusion technology are shown in Figure7.Figure 7
Optimal solution path graph and local optimal solution path graph of fusion algorithm.
(a)
Optimal solution path graph(b)
Local optimal solution path graphThe optimal path is QWE and the local optimal path is QWE. It can also be seen from the figure that the length of QW is less than that of we, so the fusion algorithm is applied at the initial stage to form a local optimal solution. The specific routing of the algorithm is shown in Figure8.Figure 8
Some regions of the optimal solution path graph and local optimal solution path graph of the fusion algorithm.
(a)
Optimal solution path diagram(b)
Local optimal solution path graph(c)
Optimal solution path 1 area(d)
Local optimal solution path 1 areaBased on the above analysis, this paper considers the local search method to improve the quality of the solution of the fusion algorithm. The fusion algorithm is used to optimize the optimal path of each cycle and exchange the adjacent points of the optimal solution of the current cycle to obtain a new solution. If the new solution is better than the optimal solution of the current period, it will replace the optimal solution of the current period. By comparing the total length of the path before and after the exchange, it is found that the result after the exchange is better than the current solution, so the path after the exchange is regarded as the optimal path of the current cycle.Based on some mathematical models, this experiment makes reasonable simulation, assuming that the number of days of each travel shall not exceed 15 days, so the experiment divides the scenic spots in the scheme with travel days D exceeding 15 days in each region. At the same time, considering the small number of travel daysD in individual regions, it is considered to merge the smaller regions. After completing the above processing, regenerate the shortest distance and shortest time matrix of each region and then solve each region to obtain the tour order of each tourist attraction of each region after merging. Through the Internet of things fusion algorithm, the tour order and travel days of each region have been calculated, and the regions are labeled and marked on the map, as shown in Figure 9. The results of the number of days required for travel in each region are shown in Table 3.Figure 9
Distribution of hypothetical travel areas.Table 3
Travel days required for each region.
Area numberNumber of days requiredArea numberNumber of days required1166102147123118154159135121011Standard deviation1.92As can be seen from the above table, the tour time in most areas is close to 15 days, which reduces the number of trips and trips and effectively shortens the round-trip time.According to the rationalization assumption, the number of days of each trip is no more than 15 days, the total number of single trips is no more than 30 days, and the number of scenic spots is no more than 4. The experiment says that these areas should be organized so that you can visit all areas in a shorter time. We sort out the specific itinerary of self-driving travel according to the regional browsing plan. After completion, the annual regional tour plan is shown in Table4.Table 4
Regional tour planning for self-driving tour route planning.
FrequencyArea numberTravel days11,62624,102633,82642,72655,925Standard deviation0.4In order to make tourism planning more scientific, experiments need to consider other modes of transportation, such as aircraft and high-speed trains. At the same time, when considering the route planning of road travel, the problem of travel cost is not considered, but travel cost is indeed a factor that must be considered when making travel plans. The subsequent improvement of the model is aims at increasing the travel cost. The mathematical model of road travel route planning is suitable for route planning in densely populated areas or within scenic spots.Path planning refers to the optimal path planning of navigation software or robots. The SM-PageRank algorithm is based on the idea of recursion. The shortest path that does not reach the vertex must be obtained from the shortest path that has reached the vertex, by clarifying the starting position and ending point, avoiding obstacles, and optimizing the path as much as possible, in order to achieve the lowest cost and the highest efficiency.


### 4.3. Build a Tourism Management Teaching Platform

In order to meet the teaching and training needs of tourism management, we improve the teaching level of teachers. The quality of teaching and the improvement of students’ learning quality and students’ learning ability and practical ability were improved. This experiment combines database technology, network technology, 3D display technology, and VR technology to construct an all-round, advanced, and visual virtual reality system to adapt to the in-depth development of the school.Smart tourism is a smart system formed by the computer as the brain and relying on the Internet of things. It integrates tourism into the interconnected system of all things and forms a smart data network based on cloud thinking. In terms of real-time data, the scenic area has been intelligently constructed to realize data visualization and effectively analyze the data and pass the data and analysis results to the scenic area management, which is conducive to improving the data ecological environment of the scenic area. The application of technologies such as the Internet of Things and cloud computing to the construction of scenic spots has brought new opportunities for the creation of smart tourism.Tourism is a strategic pillar industry of the national economy, with great potential for consumer demand. It is an important means to expand domestic demand and promote stable growth. At present, the urgent problems to be solved in tourism management are as follows: the tourism industry is facing the problem of structural adjustment, the market demand for new professionals continues to grow, and there is a shortage of relevant posts such as exhibition tour guides, senior tour guides and tourism image design. At present, the main contradiction is the contradiction between employment demand and low quality of workers. School employment is very heavy, while the market has great demand for full employment and labor force, but the quality of labor force is relatively low. From the perspective of teaching methods, teachers generally use videos, teaching slides, writing lessons, blackboard writing, etc. For indoctrination teaching, students can not operate the experiment by personally observing the experimental process. As a result, students only have theoretical knowledge, lack practical ability, and fail to truly integrate theory with practice. Teaching or student learning can achieve the desired teaching effect.According to the above situation, the system proposed in this paper needs a classroom dedicated to teaching, a large screen display, a highly configured computer host, a VR helmet, two locators, two interactive handles, and enough student seat space. The layout design of virtual reality classroom is shown in Figure10.Figure 10
VR teaching design.According to the preliminary demand analysis, three main functional modules of tourism digital training system are planned and constructed, including preclass preparation system, real-time education system, and student training. The main content of this module is to build an integrated scene, model, configuration, HR management, and collaboration module with abstract 3D scene representation as the core and collaborative visual manipulation architecture. The main function of the module is to provide the preprocessing function of electronic resources recommended by VR learning environment; integrate 3D models, materials, textures, and shaders,; support relevant data of programming and bone animation; and summarize and convert nonrelational data into relational data, such as video, audio, and documents. Especially in the case of audio and video, it is necessary to extract important information to form a connection, classify subjects and courses according to the classification tree, and build a VR education content ecosystem according to unified planning.Because the modeling of building facilities in the real scenic spot is too complex, various models and layouts in the simulation system are basically modeled with reference to the sand table model of the base. On the basis of meeting certain display effects, we simplify the model grid as much as possible to achieve a certain balance between the operation effect and efficiency of the system. After the model is built, the author needs to add materials and draw the appearance of the model. Because the process is too complex, this article will not introduce it in detail.Through the experimental construction of the simulated tour guide teaching system of tourism management specialty, there are still many deficiencies in the system R & D and research. It is also necessary to increase the virtual scenic spot library of the tourism digital training system and optimize the scene rendering. In addition, it is also necessary to apply the construction ideas and schemes of the system to other majors, build a collection of professional teaching and training applications of virtual reality and vocational education, create a perfect virtual reality intelligent education platform, and promote the development of intelligent vocational education to a higher and faster track.


## 4.1. Construction of Experimental Platform

This experiment will use Solr and Nutch to build an information retrieval experiment platform, capture experimental data through Nutch, modify Nutch’s existing scoring algorithm, and apply the SM-P algorithm to realize the user interest module [27]. This paper uses Nutch, Solr, and other tools to build the Intelligent Tourism search experimental platform, and its overall structure is shown in Figure 3.Figure 3
Overall platform architecture.Nutch is a Lucene-based search engine system. It not only provides all the tools required by the search engine but also can crawl billions of web pages every month and index these crawled web pages. However, in order to meet the distributed requirements, Nutch has gradually become a crawler tool, and the retrieval function has been given to other tools. Solr is an enterprise search engine server that can index the retrieved data sources. Solr provides a search interface for application search. Like Lucene, Solr provides full-text search, keyword highlighting, and management interfaces. Configuring the Chinese word segmentation method is a necessary step before indexing with Solr [28]. In this experiment, the IK analyzer word segmentation tool is used to finally configure the word segmentation effect.In this experiment, five search words (mountain climbing, travel agency, food, scenic spots, and outbound tourism) were selected for query. When comparing the results, the first 50 items of the result set returned by each search word were taken as the investigation objects. The comparison of query statistics and accuracy is shown in Table1.Table 1
Statistics of search results of traditional PageRank and SM-PageRank algorithms.
Key wordsPageRank algorithmSM-PageRank algorithmSearch resultsSelectionRelated webpagesAccuracySearch resultsSelectionRelated webpagesAccuracyClimb mountains258503876%256504386%Travel agency279512956.9%257515098%Delicacy208523363.5%324524994.2%Scenic spots206533158.5%296534075.5%Outbound tourism205543870.4%329543972.2%Six testers were interviewed to search the above keywords and score the satisfaction of the search results. The comparison of accuracy and satisfaction of the two algorithms is shown in Figure4.Figure 4
Comparison of accuracy and satisfaction of PageRank and SM-PageRank.According to the above figure and table, the optimized SM-PageRank algorithm is better than the traditional PageRank algorithm.Then, five search words (mountain climbing, travel agency, food, scenic spots, and outbound tourism) are also selected for query, and the correlation between the experimental search results and the query content is manually compared through SM-PageRank algorithm and Google search engine [29]. In the query results, the platform takes the top 50 returned results list as the research basis, and the top 100 Google search results are analyzed and compared. The query statistics are shown in Table 2.Table 2
Sorting comparison of query results.
Key wordsSM-PageRank algorithmGoogle searchSearch resultsSelectionRelated webpagesAccuracySearch resultsSelectionRelated webpagesAccuracyClimb mountains258503876%123061004343%Travel agency279512956.9%118551005656%Delicacy208523363.5%155521007676%Scenic spots206533158.5%129941007070%Outbound tourism205543870.4%135891006767%Because the experimental platform takes a short time to grab web pages and the number of web pages is less than that of Google search engine, the total number of returned results is also less, which is not as good as Google search engine in recall.To establish a secondary sorting algorithm, the experiment needs to establish a user interest model for users. This experiment establishes a user’s interest model through user registration. The contents required to be filled in by the user during registration (such as route, diet, accommodation, comments, etc.) are of interest to the user. After the user registration is completed, the results of secondary sorting search are given to establish a user interest model for the user. The results returned by secondary sorting are more in line with user needs. Figure5 shows the comparison of SM-PageRank and Google search accuracy.Figure 5
Comparison of SM-PageRank and secondary sorting accuracy and satisfaction.By comparing the above experimental results, we conclude that the search accuracy of SM-PageRank algorithm is higher than that of conventional PageRank algorithm, and the secondary ranking based on user interest model makes the search results more appropriate. As can be seen from the above figure, the search results of SM-PageRank can better meet the needs of users.After connecting the major scenic spots, this algorithm can also count the passenger flow, including real-time passenger flow statistics and attribute distribution statistics. Figure6 shows the relationship between passenger flow and temperature according to system statistics.Figure 6
Passenger flow statistics interface.


## 4.2. Fusion Technology for Solving Travel Problems

Traditional tourist routes are usually one or more fixed routes designed by travel agencies, which are suitable for fixed routes and group tourism. Traditional tourism routes are the crystallization of a tourism experience, which usually takes a certain time to design [30]. Artificial intelligence has greatly improved the rationality of personal travel routes. The integration of Internet of things into route planning has greatly promoted the intelligence of travel route planning. It is more scientific, persuasive, and efficient to solve the problem of tourism route planning by establishing a model and integrating the Internet of things.Internet of things fusion is one of the effective algorithms to solve the problem of travel route planning. In order to apply Internet of things fusion to solve the problem of tourism route planning, it is necessary to improve the Internet of things fusion, so that the improved Internet of things fusion can obtain the optimal solution with higher probability in the shortest time. Internet of things integration to solve the traveling salesman problem mainly depends on the path selection strategy, pheromone update rules, and the setting of relevant parameters, and the improvement of Internet of things integration mainly focuses on these three aspects. The improvement of path selection strategy mainly aims at the defect that Internet of things fusion is easy to fall into local optimal solution. By improving the path selection probability, local search and other strategies, it is possible to help the Internet of things fusion jump out of the local optimal solution, so as to improve the quality of the solution. Pheromone accumulation and feedback are also the improvement focus of pheromone update rules, which is a better method to guide pheromones to the best path and avoid the influence of pheromones on sensors on poor paths. For the setting of relevant parameters, different traveling salesman problems can be solved more effectively. At present, there is no very good mathematical proof to help judge the quality of parameters. Generally speaking, the range of parameters is determined by experience, and then, the size of parameters is dynamically adjusted.The optimal solution path map and local optimal solution path map of Internet of things fusion technology are shown in Figure7.Figure 7
Optimal solution path graph and local optimal solution path graph of fusion algorithm.
(a)
Optimal solution path graph(b)
Local optimal solution path graphThe optimal path is QWE and the local optimal path is QWE. It can also be seen from the figure that the length of QW is less than that of we, so the fusion algorithm is applied at the initial stage to form a local optimal solution. The specific routing of the algorithm is shown in Figure8.Figure 8
Some regions of the optimal solution path graph and local optimal solution path graph of the fusion algorithm.
(a)
Optimal solution path diagram(b)
Local optimal solution path graph(c)
Optimal solution path 1 area(d)
Local optimal solution path 1 areaBased on the above analysis, this paper considers the local search method to improve the quality of the solution of the fusion algorithm. The fusion algorithm is used to optimize the optimal path of each cycle and exchange the adjacent points of the optimal solution of the current cycle to obtain a new solution. If the new solution is better than the optimal solution of the current period, it will replace the optimal solution of the current period. By comparing the total length of the path before and after the exchange, it is found that the result after the exchange is better than the current solution, so the path after the exchange is regarded as the optimal path of the current cycle.Based on some mathematical models, this experiment makes reasonable simulation, assuming that the number of days of each travel shall not exceed 15 days, so the experiment divides the scenic spots in the scheme with travel days D exceeding 15 days in each region. At the same time, considering the small number of travel daysD in individual regions, it is considered to merge the smaller regions. After completing the above processing, regenerate the shortest distance and shortest time matrix of each region and then solve each region to obtain the tour order of each tourist attraction of each region after merging. Through the Internet of things fusion algorithm, the tour order and travel days of each region have been calculated, and the regions are labeled and marked on the map, as shown in Figure 9. The results of the number of days required for travel in each region are shown in Table 3.Figure 9
Distribution of hypothetical travel areas.Table 3
Travel days required for each region.
Area numberNumber of days requiredArea numberNumber of days required1166102147123118154159135121011Standard deviation1.92As can be seen from the above table, the tour time in most areas is close to 15 days, which reduces the number of trips and trips and effectively shortens the round-trip time.According to the rationalization assumption, the number of days of each trip is no more than 15 days, the total number of single trips is no more than 30 days, and the number of scenic spots is no more than 4. The experiment says that these areas should be organized so that you can visit all areas in a shorter time. We sort out the specific itinerary of self-driving travel according to the regional browsing plan. After completion, the annual regional tour plan is shown in Table4.Table 4
Regional tour planning for self-driving tour route planning.
FrequencyArea numberTravel days11,62624,102633,82642,72655,925Standard deviation0.4In order to make tourism planning more scientific, experiments need to consider other modes of transportation, such as aircraft and high-speed trains. At the same time, when considering the route planning of road travel, the problem of travel cost is not considered, but travel cost is indeed a factor that must be considered when making travel plans. The subsequent improvement of the model is aims at increasing the travel cost. The mathematical model of road travel route planning is suitable for route planning in densely populated areas or within scenic spots.Path planning refers to the optimal path planning of navigation software or robots. The SM-PageRank algorithm is based on the idea of recursion. The shortest path that does not reach the vertex must be obtained from the shortest path that has reached the vertex, by clarifying the starting position and ending point, avoiding obstacles, and optimizing the path as much as possible, in order to achieve the lowest cost and the highest efficiency.


## 4.3. Build a Tourism Management Teaching Platform

In order to meet the teaching and training needs of tourism management, we improve the teaching level of teachers. The quality of teaching and the improvement of students’ learning quality and students’ learning ability and practical ability were improved. This experiment combines database technology, network technology, 3D display technology, and VR technology to construct an all-round, advanced, and visual virtual reality system to adapt to the in-depth development of the school.Smart tourism is a smart system formed by the computer as the brain and relying on the Internet of things. It integrates tourism into the interconnected system of all things and forms a smart data network based on cloud thinking. In terms of real-time data, the scenic area has been intelligently constructed to realize data visualization and effectively analyze the data and pass the data and analysis results to the scenic area management, which is conducive to improving the data ecological environment of the scenic area. The application of technologies such as the Internet of Things and cloud computing to the construction of scenic spots has brought new opportunities for the creation of smart tourism.Tourism is a strategic pillar industry of the national economy, with great potential for consumer demand. It is an important means to expand domestic demand and promote stable growth. At present, the urgent problems to be solved in tourism management are as follows: the tourism industry is facing the problem of structural adjustment, the market demand for new professionals continues to grow, and there is a shortage of relevant posts such as exhibition tour guides, senior tour guides and tourism image design. At present, the main contradiction is the contradiction between employment demand and low quality of workers. School employment is very heavy, while the market has great demand for full employment and labor force, but the quality of labor force is relatively low. From the perspective of teaching methods, teachers generally use videos, teaching slides, writing lessons, blackboard writing, etc. For indoctrination teaching, students can not operate the experiment by personally observing the experimental process. As a result, students only have theoretical knowledge, lack practical ability, and fail to truly integrate theory with practice. Teaching or student learning can achieve the desired teaching effect.According to the above situation, the system proposed in this paper needs a classroom dedicated to teaching, a large screen display, a highly configured computer host, a VR helmet, two locators, two interactive handles, and enough student seat space. The layout design of virtual reality classroom is shown in Figure10.Figure 10
VR teaching design.According to the preliminary demand analysis, three main functional modules of tourism digital training system are planned and constructed, including preclass preparation system, real-time education system, and student training. The main content of this module is to build an integrated scene, model, configuration, HR management, and collaboration module with abstract 3D scene representation as the core and collaborative visual manipulation architecture. The main function of the module is to provide the preprocessing function of electronic resources recommended by VR learning environment; integrate 3D models, materials, textures, and shaders,; support relevant data of programming and bone animation; and summarize and convert nonrelational data into relational data, such as video, audio, and documents. Especially in the case of audio and video, it is necessary to extract important information to form a connection, classify subjects and courses according to the classification tree, and build a VR education content ecosystem according to unified planning.Because the modeling of building facilities in the real scenic spot is too complex, various models and layouts in the simulation system are basically modeled with reference to the sand table model of the base. On the basis of meeting certain display effects, we simplify the model grid as much as possible to achieve a certain balance between the operation effect and efficiency of the system. After the model is built, the author needs to add materials and draw the appearance of the model. Because the process is too complex, this article will not introduce it in detail.Through the experimental construction of the simulated tour guide teaching system of tourism management specialty, there are still many deficiencies in the system R & D and research. It is also necessary to increase the virtual scenic spot library of the tourism digital training system and optimize the scene rendering. In addition, it is also necessary to apply the construction ideas and schemes of the system to other majors, build a collection of professional teaching and training applications of virtual reality and vocational education, create a perfect virtual reality intelligent education platform, and promote the development of intelligent vocational education to a higher and faster track.


## 5. Discussion

The tourism teaching system designed in this article is exactly the key task faced by the education industry and the tourism industry. The system has been applied to tourism talent training colleges to allow graduates to meet the actual needs of the tourism industry and conform to the development direction of the tourism industry. Although this paper proposes a secondary sorting algorithm based on user interest model and establishes and studies the user model, there are still many imperfections. Experiments should get users’ interest from more channels and then deeply dig into users’ points of interest and establish a more reasonable model of interest. In model updating, how to make model updating more effective and how to establish long-term and short-term models for users are the next research direction. In the actual planning of intelligent travel route, this paper adopts real data and reasonable hypothetical data. Therefore, we can further consider how to design an algorithm that can automatically search and obtain relevant data through the network and other aspects, which reflects the intelligent side. In addition, this paper introduces the application process of virtual reality technology in the virtual reality teaching platform of tourism management specialty, but there is no detailed explanation of the technical implementation, including three-dimensional modeling technology, scene loading and setting, database connection, interactive design, simulation effect, and project release.


## 6. Conclusions

In the current tourism education industry, the understanding of informatization in relevant tourism professional education only stays at the primary level of the internal management information system. The experimental conclusions are as follows. Through experimental calculations, it is found that this paper proposes the SM-PageRank algorithm based on Nutch and Solr and the secondary ranking based on the user interest model, which makes the information query more reliable and accurate. The advantages of the algorithm are mainly manifested in the calculation speed and accuracy. Compared with the traditional algorithm, the average accuracy rate is increased by 20.1%. Compared with the Google search, the average accuracy rate is increased by 2.6%, which can meet the search needs of users.



---

*Source: 6034500-2022-01-18.xml*