# Analysis of the Construction of Big Data Platform in Scenic Spots to Increase the Number of Tourists: Taking Sports Group Performance as an Example

Authors: Lin Yu; Li Hua; Jiaran Ding
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

The construction of a big data platform is the basis for improving the service level of scenic spots, and it is also a new media way to increase the number of tourists. At present, the scenic spot platform lacks effective evaluation methods and cannot analyze massive data, resulting in an insufficient increase in the number of tourists. Therefore, this paper analyzes the construction of the big data platform from the perspective of sports group performance, aiming at promoting the increase in the number of tourists in scenic spots. Firstly, the continuous clustering sampling method is used to make statistics on the massive tourist data in the platform. Secondly, the equidistant sampling coefficient is added to the sample data to ensure the validity of tourist data.


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## Body

## 1. Introduction

Online platform construction is a key period for the transformation, development, and infrastructure construction of scenic spots, and it has also become an “opportunity window period” [1]. Because the scenic spots in China receive tourists mainly offline and take traditional ticket purchase as the main business, tourists cannot get full mood, vision, sensory experience, cooperation, team formation, and other abilities. A survey of tourism services in scenic spots in medium-level countries found that the construction rate of online platforms in northeast, middle level, and western mountainous areas of medium-level countries was 32.3% [2], which was significantly lower than that of the requirements of global tourism platform construction, while the ticket purchase rate of offline tourists was consistent with that of the global tourism level [3]. At present, the platform construction of scenic spots has not achieved satisfactory results, and there are controversies about the role of increasing the number of tourists. At the same time, the existing tourist reception and the transformation mode in the scenic spot belong to the traditional mode, without digital and intelligent construction. The platform construction of foreign scenic spots is relatively perfect and has achieved satisfactory results, while the platform construction of domestic scenic spots is still in progress. In order to better build the scenic spot platform, it is necessary to analyze the increase in the number of tourists, especially under the background of big data. Based on the construction of the big data platform, this paper analyzes the effect of the platform on improving the number of tourists, aiming at increasing the number of tourists in scenic spots and improving the satisfaction of tourists. Foreign scholars have obvious opinions on the platform construction of scenic spots and think that scenic spots should strengthen the platform construction, especially the big data platform, so as to improve the reception efficiency of tourists. At the same time, foreign scholars believe that the construction of the big data platform can bring more services to tourists and improve their satisfaction. Domestic scholars also support the construction of the big data platform, but the construction of domestic scenic spots has its own shortcomings. How to complete the construction of the big data platform under the existing conditions is an urgent problem to be solved. Therefore, it is necessary to make a statistical analysis of the big data platform, understand the attraction of the big data platform to tourists, better build the big data platform, and provide corresponding data and policy support.Therefore, how to convert online ticket purchase for tourists from Northeast China, Central China, and West China has become a high-level point and hot spot studied by domestic scholars. Some scholars, such as Thumrongvut et al. [4], believe that the smart platform construction project proposed by the state in 2018 is an online transformation measure for tourists, which can increase the number of tourists. Some scholars, such as Shojatalab et al. [5], believe that the smart platform construction project is extensive and lacks pertinence, which is not suitable for Jiangxi, Guizhou, and other ethnic minority areas. Based on the above reasons, martial arts group performances are universal, ornamental, and extensive in the construction of sports group projects in scenic spots. This article takes martial arts group performances as an example to analyze sports group performances in scenic spots. With the support of the Jiangxi Provincial Tourism Bureau, this paper conducts a special investigation on the online transformation of tourists for martial arts group performances, and compares the improvement of tourists before and after the implementation of the online transformation, aiming to find a way to increase the number of tourists in medium-level areas.


## 2. Research Content of Smart Platform Project

### 2.1. Content of the Study

According to the regulation of the middle level of smart platform construction project put forward by the state in 2018, 982 tourists from 7 online platforms in Jiangxi tourist attractions were selected as the research objects, and the tour leader was informed of the purpose, significance, and function of the project, as well as the privacy problems that may arise at the middle level during the investigation. Among them, 1022 tourists meet the requirements, accounting for 93.3% of the total. Inclusion criteria of the tourists were as follows: (1) tourists in tourist attractions in Jiangxi Province who have an annual family income of less than 4000RMB and have nucleic acid certificates in the local area; (2) first-time visitors to tourist attractions in Jiangxi Province and those who have no high-level violation of discipline or behavior; and (3) those who have an informed consent form signed by the team leader and approved by the scenic tourism association. Exclusion criteria of the tourists were as follows: (1) those who give up an online ticket purchase for less than 3 hours or tourists with low individual volume of less than 250RMB; (2) tourists suffering from mental illness; and (3) tourists who quit at a medium level or fail to travel according to the requirements of the smart platform [6].


### 2.2. Mathematical Methods of Research

The sample extraction method is a combination of the continuous sampling method and the random equidistant sampling method, and spot checks are made on the number of tourists. Assuming sample size is the presupposition of tourist contribution rate before intervention and the presupposition of contribution rate after intervention, the confidence level is 95%, and the test validity is 90%. The weight factor is obtained from the number of tourists in each district of the scenic spot/the total number of tourists in the scenic spot. Then, five tourists are selected from each tourist area, and the teams with 1 ∼ 3 tourists are sorted. Finally, the continuous sampling results are obtained. The calculation process is shown in the following formula:(1)r=w⋅∑A⋅Bα⋅β.The tourists who draw middle-level teams are sorted according to their initials, and the random equidistant sampling method is adopted to obtain continuous sampling results. The calculation process is shown in the following formula:(2)R=w⋅∑A⋅Bα⋅β|m.Among them, the names of investigators are sorted. A total of 45 surveys were selected from each sample tour group, 250 surveys were selected from each tourist area, and 982 surveys were selected from tourist attractions as special survey objects. Tourist volume calculation is to use smart platform projects to calculate tourist volumeM and operate according to system instructions. Training T=2.5h and operation practicet=4h for relevant personnel before operation of intelligent platform. Each operation is guided by the instructor z=3. Among them, one teacher with average level has more than Z=2 years of experience in the tourism survey. When calculating the number of tourists, three calculations should be carried out[7]: the minimum value min∑M and the maximum valuemax∑M, which should be eliminated; and the average value mean∑M, which should be taken into calculation. The number of tourists is calculated in tens of thousands with an accuracy of 0.1; individual passenger volume is calculated in thousands with an accuracy of 0.1. We should adopt the recommended standards of global tourism standards Bi: The number of individual customers is less than 1000 people/day, which is a low number of individual customers B1; online tourists less than 25% have a low conversion rate b1; the number of individual customers is 1000∼2000 people/day, which is a medium level of individual customers B2; 25∼70% of online tourists have a medium conversion rate b2; the number of individual customers is more than 2000 people/day, which is a high number of individual customers B3; and 70∼100% of online tourists have a high conversion rate b3 [8]. According to the above parameters, the calculation process of tourist quantity can be obtained, as shown in the following formula:(3)M=∑T,t=1T=2.5,t=4z⋅∑max∑M−min∑Mmean∑M−|m.The measurement of satisfaction and feedback rate is to investigate the satisfaction and feedback rate of tourists. The score of self-made Satisfaction Questionnaire of Smart Platform Project isC, and the score of Feedback Rate Questionnaire of Smart Platform Project is D. Investigators fill out the form through an online survey system. All questionnaires have 10 questions, each with 10 points, a total of 100 points, and the survey results are counted. The calculation process is shown in the following formula:(4)totle=∑iCi⋅Di100.Assuming that the function of the big data platform isplat, the amount of data in the data platform is data, and the number of tourists served by the big data platform is Z, the calculation of the big data function is as shown in the following formula:(5)plat=∑iω⋅dataizi.Among them,ω is the adjustment coefficient of the platform, such as policy adjustment and system adjustment. Assume that the relationship between big data platform construction and tourists is R, sports group performance is J, and the relationship between platform construction and tourists is calculated, as shown in the following formula:(6)R=J⇄z∑iω⋅dataii.With the questionnaire survey, referring to the relevant research data of intelligent platform construction and combining with the characteristics of Jiangxi tourism, this paper designs a questionnaire on the number of tourists. The content includes the basic situation survey of tourists, the survey of tourists’ quantity, and the online transformation consciousness survey of tourist leaders. The contents of the survey involve the basic situation of tourists, the online transformation history of tourists, the satisfaction of tourists and the feedback 24 hours after the trip, and the online transformation consciousness of tourist leaders [9].


## 2.1. Content of the Study

According to the regulation of the middle level of smart platform construction project put forward by the state in 2018, 982 tourists from 7 online platforms in Jiangxi tourist attractions were selected as the research objects, and the tour leader was informed of the purpose, significance, and function of the project, as well as the privacy problems that may arise at the middle level during the investigation. Among them, 1022 tourists meet the requirements, accounting for 93.3% of the total. Inclusion criteria of the tourists were as follows: (1) tourists in tourist attractions in Jiangxi Province who have an annual family income of less than 4000RMB and have nucleic acid certificates in the local area; (2) first-time visitors to tourist attractions in Jiangxi Province and those who have no high-level violation of discipline or behavior; and (3) those who have an informed consent form signed by the team leader and approved by the scenic tourism association. Exclusion criteria of the tourists were as follows: (1) those who give up an online ticket purchase for less than 3 hours or tourists with low individual volume of less than 250RMB; (2) tourists suffering from mental illness; and (3) tourists who quit at a medium level or fail to travel according to the requirements of the smart platform [6].


## 2.2. Mathematical Methods of Research

The sample extraction method is a combination of the continuous sampling method and the random equidistant sampling method, and spot checks are made on the number of tourists. Assuming sample size is the presupposition of tourist contribution rate before intervention and the presupposition of contribution rate after intervention, the confidence level is 95%, and the test validity is 90%. The weight factor is obtained from the number of tourists in each district of the scenic spot/the total number of tourists in the scenic spot. Then, five tourists are selected from each tourist area, and the teams with 1 ∼ 3 tourists are sorted. Finally, the continuous sampling results are obtained. The calculation process is shown in the following formula:(1)r=w⋅∑A⋅Bα⋅β.The tourists who draw middle-level teams are sorted according to their initials, and the random equidistant sampling method is adopted to obtain continuous sampling results. The calculation process is shown in the following formula:(2)R=w⋅∑A⋅Bα⋅β|m.Among them, the names of investigators are sorted. A total of 45 surveys were selected from each sample tour group, 250 surveys were selected from each tourist area, and 982 surveys were selected from tourist attractions as special survey objects. Tourist volume calculation is to use smart platform projects to calculate tourist volumeM and operate according to system instructions. Training T=2.5h and operation practicet=4h for relevant personnel before operation of intelligent platform. Each operation is guided by the instructor z=3. Among them, one teacher with average level has more than Z=2 years of experience in the tourism survey. When calculating the number of tourists, three calculations should be carried out[7]: the minimum value min∑M and the maximum valuemax∑M, which should be eliminated; and the average value mean∑M, which should be taken into calculation. The number of tourists is calculated in tens of thousands with an accuracy of 0.1; individual passenger volume is calculated in thousands with an accuracy of 0.1. We should adopt the recommended standards of global tourism standards Bi: The number of individual customers is less than 1000 people/day, which is a low number of individual customers B1; online tourists less than 25% have a low conversion rate b1; the number of individual customers is 1000∼2000 people/day, which is a medium level of individual customers B2; 25∼70% of online tourists have a medium conversion rate b2; the number of individual customers is more than 2000 people/day, which is a high number of individual customers B3; and 70∼100% of online tourists have a high conversion rate b3 [8]. According to the above parameters, the calculation process of tourist quantity can be obtained, as shown in the following formula:(3)M=∑T,t=1T=2.5,t=4z⋅∑max∑M−min∑Mmean∑M−|m.The measurement of satisfaction and feedback rate is to investigate the satisfaction and feedback rate of tourists. The score of self-made Satisfaction Questionnaire of Smart Platform Project isC, and the score of Feedback Rate Questionnaire of Smart Platform Project is D. Investigators fill out the form through an online survey system. All questionnaires have 10 questions, each with 10 points, a total of 100 points, and the survey results are counted. The calculation process is shown in the following formula:(4)totle=∑iCi⋅Di100.Assuming that the function of the big data platform isplat, the amount of data in the data platform is data, and the number of tourists served by the big data platform is Z, the calculation of the big data function is as shown in the following formula:(5)plat=∑iω⋅dataizi.Among them,ω is the adjustment coefficient of the platform, such as policy adjustment and system adjustment. Assume that the relationship between big data platform construction and tourists is R, sports group performance is J, and the relationship between platform construction and tourists is calculated, as shown in the following formula:(6)R=J⇄z∑iω⋅dataii.With the questionnaire survey, referring to the relevant research data of intelligent platform construction and combining with the characteristics of Jiangxi tourism, this paper designs a questionnaire on the number of tourists. The content includes the basic situation survey of tourists, the survey of tourists’ quantity, and the online transformation consciousness survey of tourist leaders. The contents of the survey involve the basic situation of tourists, the online transformation history of tourists, the satisfaction of tourists and the feedback 24 hours after the trip, and the online transformation consciousness of tourist leaders [9].


## 3. Results

The intelligent platform project is used to input, request, and calculate the number of tourists. The t-test was used for continuous variables, and the chi-squared test was used for the comparison of mean values among teams at all levels.


### 3.1. General Tourism Survey Information

There is no significant difference in the basic indicators such as gender, age, income, and region of tourists, and there is no statistical significance (P>0.05) as shown in Table 1.Table 1
General information of tourists.
VariableNumber of peopleNumber of people converted onlineConversion rate (%)FPAge0.2720.02120–3034124270.9730–40241280116.1840–60249361144.98Gender0.7130.238Male1845831.52Female247725293.52Wushu content0.7280.018Martial arts area156334214.10Sword area28217662.41Boxing area1786536.52Wrestling area2129745.75A juggling area22715367.40Sectarian district29417659.526Other areas32519860.92Income0.8220.1012000∼400035314240.231000∼200037626269.68Under 100033513239.40Tourism experience0.7020.2011∼2 years38917244.223∼4 years39818245.734∼5 years28217260.995∼6 years39518346.33Ticket purchase method0.9580.272Under online27716760.29Online2229844.14According to the data in Table1, the respondents are mainly between 20 and 30 years old and mainly women. Among them, traditional performing arts such as martial arts and swords are more attractive; most tourists earn 1000∼2000; and most of them have 1∼4 years of tourism experience. At the same time, tourists’ online purchase methods are the main ones, while traditional purchase methods are the auxiliary ones. Therefore, the respondents are mainly young people, low-income people with less travel experience, and these people mainly buy online. Therefore, the role and significance of the construction of the big data platform in scenic spots are very obvious, and the potential in the future is also great. The overall data difference degree of surveyed tourists is shown in Figure 1.Figure 1
Difference degree of tourist data.As can be seen from Figure1, the difference in tourists’ data is small and there is no statistical difference, so relevant statistical analysis can be carried out. Among them, tourism experience has the largest proportion, followed by martial arts, age, and finally ticket purchase method, gender, and income. Therefore, the big data platform should pay attention to tourists with 1–3 years of tourism experience and then track women who are interested in martial arts. At the same time, the above objects are recommended for online ticket purchase, so as to expand the user group. In addition, the big data platform should send information to customers who regularly love women and martial arts to attract tourists to pay attention to the corresponding contents of scenic spots.


### 3.2. Tourist Volume Status

There is information about 982 valid tourists in the middle level of tourist volume, with 9.52 ± 1.82 individual tourists and 8.01 ± 26.9 average tourists, as shown in Table2.Table 2
Measurement results of group tourists and individual tourists at all levels of the survey team tourists.
Team rankIndividual passenger volume (thousands)Number of tourists (10,000)Level 18.75 ± 1.1311.25 ± 1.36Observation group10.03 ± 1.25b15.02 ± 1.02bControl group6.25 ± 1.25a10.05 ± 0.68aLevel 210.02 ± 1.1413.75 ± 0.68Observation group13.75 ± 1.25b10.02 ± 1.36bControl group6.25 ± 0.919a6.25 ± 0.90aLevel 312.52 ± 0.568.75 ± 1.13Observation group10.01 ± 0.93b6.25 ± 1.36bControl group11.25 ± 1.02a8.75 ± 1.25aOverall results10.00 ± 0.6812.53 ± 1.36Observation group7.53 ± 1.02b6.25 ± 0.68bControl group7.54 ± 0.56a8.75 ± 0.91aCompared with the observation group,aP>0.05; compared with the control group, bP<0.05.The low individual volume rate, online abandonment rate, and complaint rate of group tourists were 6.8%, 9.0%, and 10.1%, respectively, which had no significant difference with the observation group (allP>0.05) but had a significant difference with the requirements of Jiangxi industry (all P<0.05). There are statistical differences in online abandonment rates among teams of different grades. With the increase in grades, the online abandonment rates of group tourists decrease (all P<0.05), which has no significant difference with the observation group (all P>0.05) but has a significant difference with the requirements of industries in Jiangxi (all P<0.05). The overall results are shown in Table 2.It can be seen from Figure2 that the number of tourists in the observation group is significantly higher than that in the control group, and there is an obvious difference, so the number of tourists in the observation group is larger, and the intelligent platform has better attraction effect on the number of tourists [10]. For further analysis, the satisfaction and feedback rates of different groups are compared, and the results are shown in Table 3.Figure 2
Measurement of tourist quantity in different groups.Table 3
Comparison of satisfaction among teams at all levels.
Team rankBasic satisfactionSatisfiedVery satisfiedLevel 161.25 ± 1.3677.53 ± 0.5671.25 ± 1.25Observation group56.25 ± 1.02b66.25 ± 1.25b67.5 ± 1.02bControl group63.75 ± 0.91a60.24 ± 1.13a56.25 ± 0.56aLevel 257.5 ± 0.7963.75 ± 0.6861.25 ± 0.56Observation group58.75 ± 1.36b62.5 ± 1.25b66.25 ± 1.36bControl group66.25 ± 1.02a56.25 ± 0.90a76.25 ± 0.56aLevel 377.51 ± 0.6872.53 ± 0.9281.25 ± 0.92Observation group68.75 ± 0.92b77.59 ± 1.13b71.25 ± 0.56bControl group71.25 ± 0.79a76.25 ± 0.68a71.25 ± 1.13aOverall results60.23 ± 1.1363.75 ± 0.5677.58 ± 0.93Observation group57.53 ± 1.25b65.65 ± 0.94b62.53 ± 0.94bControl group61.25 ± 1.36a81.25 ± 0.68a71.25 ± 1.23aCompared with the observation group,aP>0.05; compared with the control group, bP<0.05.Comparing the satisfaction changes among different groups, the results are shown in Figure3.Figure 3
Satisfaction changes of different groups.It can be seen from Figure3 that the satisfactory rate of the observation group changes little, which is significantly better than that of the control group. The data change range of the observation group is smaller than that of the control group, and the data nodes of the observation group are less than those of the control group, which shows that the data change of the observation group is more stable, and it also shows that the scenic big data platform can provide stable customer volume, which is superior to the traditional customer acceptance mode. In addition, the data of the observation group changed greatly at the beginning and gradually decreased at the later stage. In the middle and later period, the change range of the control group also gradually increased, which further shows that the data change of the control group is unstable. At the same time, the data indicate that the construction of the smart platform project can significantly improve the satisfaction of tourists and enhance the attractiveness of Wushu group performances [11], as shown in Table 4.Table 4
Comparison of feedback rates between teams at all levels.
VariableClass 1 groupGrade 2 groupGrade 3 groupLow individual customer volume rate (%)25.7130.3627.14Observation group28.93b28.93b17.86bControl group16.43a21.43a24.29aOnline abandonment rate (%)43.7528.7525.00Observation group28.75b30.80b18.75bControl group30.00ac27.50ac30.00aComplaint rate (%)2.032.502.50Observation group6.25b2.00b3.75bControl group1.25a5.00a3.02aRepurchase rate (%)18.7531.2518.75Observation group28.75bc25.00b26.25bControl group31.25a22.50ac27.50acBad review rate (%)7.501.251.00Observation group7.50b6.25b2.75bControl group3.75a8.75a3.10acCompared with the observation group,aP>0.05; compared with the control group, bP<0.05; compared with the observation group, cP<0.05.


### 3.3. Online Contribution Rate of Group Tourists

The average online contribution rate of tourists in 2019 year’s survey team is 52.0%, among which the contribution rate of an equal-level low-level line is 41.0%, the contribution rate of a medium-level equal-level line is 11.1%, and the contribution rate of a high-level line is 0.9%, as shown in Table5.Table 5
Online contribution rate of teams at all levels.
VariableClass 1 groupGrade 2 groupGrade 3 groupContribution rate on a low horizontal line76.5384.6981.63Observation group66.33b78.57b74.49bControl group86.73a83.67a64.29aContribution rate on a medium horizontal line82.6581.6378.57Observation group61.22b74.49b61.22bControl group68.37a85.71a78.57aContribution rate on a higher horizontal line71.4366.3381.63Observation group62.24b78.57b77.55bControl group79.59a69.39a81.63aCompared with the observation group,aP>0.05; compared with the control group, bP<0.05.It can be seen that the contribution rate on the medium level is obvious among the first-level teams, while the high level is not statistically significant among the second-level teams. Among them, in terms of low-level contribution rate, there are significant differences between grades 1 and 3; in the middle level, there are significant differences in grades 1 and 2 but no significant differences in Grade 3; and in the aspect of high-level contribution rate, there are significant differences among grades 1–3. It can be seen that there are significant differences between grades 1 and 3 in low and high contribution rates, while there is little difference between grades 1 and 3 in medium contribution rate. Therefore, the contribution rate of grades 1–3 is polarized, and the big data platform should track the tourists of the two grades and formulate different attraction schemes.The range of online ticket purchase for online transformation is particularly important for level 3 teams. And it is significantly lower than the contribution rate of 71% of domestic online tourists [12].


## 3.1. General Tourism Survey Information

There is no significant difference in the basic indicators such as gender, age, income, and region of tourists, and there is no statistical significance (P>0.05) as shown in Table 1.Table 1
General information of tourists.
VariableNumber of peopleNumber of people converted onlineConversion rate (%)FPAge0.2720.02120–3034124270.9730–40241280116.1840–60249361144.98Gender0.7130.238Male1845831.52Female247725293.52Wushu content0.7280.018Martial arts area156334214.10Sword area28217662.41Boxing area1786536.52Wrestling area2129745.75A juggling area22715367.40Sectarian district29417659.526Other areas32519860.92Income0.8220.1012000∼400035314240.231000∼200037626269.68Under 100033513239.40Tourism experience0.7020.2011∼2 years38917244.223∼4 years39818245.734∼5 years28217260.995∼6 years39518346.33Ticket purchase method0.9580.272Under online27716760.29Online2229844.14According to the data in Table1, the respondents are mainly between 20 and 30 years old and mainly women. Among them, traditional performing arts such as martial arts and swords are more attractive; most tourists earn 1000∼2000; and most of them have 1∼4 years of tourism experience. At the same time, tourists’ online purchase methods are the main ones, while traditional purchase methods are the auxiliary ones. Therefore, the respondents are mainly young people, low-income people with less travel experience, and these people mainly buy online. Therefore, the role and significance of the construction of the big data platform in scenic spots are very obvious, and the potential in the future is also great. The overall data difference degree of surveyed tourists is shown in Figure 1.Figure 1
Difference degree of tourist data.As can be seen from Figure1, the difference in tourists’ data is small and there is no statistical difference, so relevant statistical analysis can be carried out. Among them, tourism experience has the largest proportion, followed by martial arts, age, and finally ticket purchase method, gender, and income. Therefore, the big data platform should pay attention to tourists with 1–3 years of tourism experience and then track women who are interested in martial arts. At the same time, the above objects are recommended for online ticket purchase, so as to expand the user group. In addition, the big data platform should send information to customers who regularly love women and martial arts to attract tourists to pay attention to the corresponding contents of scenic spots.


## 3.2. Tourist Volume Status

There is information about 982 valid tourists in the middle level of tourist volume, with 9.52 ± 1.82 individual tourists and 8.01 ± 26.9 average tourists, as shown in Table2.Table 2
Measurement results of group tourists and individual tourists at all levels of the survey team tourists.
Team rankIndividual passenger volume (thousands)Number of tourists (10,000)Level 18.75 ± 1.1311.25 ± 1.36Observation group10.03 ± 1.25b15.02 ± 1.02bControl group6.25 ± 1.25a10.05 ± 0.68aLevel 210.02 ± 1.1413.75 ± 0.68Observation group13.75 ± 1.25b10.02 ± 1.36bControl group6.25 ± 0.919a6.25 ± 0.90aLevel 312.52 ± 0.568.75 ± 1.13Observation group10.01 ± 0.93b6.25 ± 1.36bControl group11.25 ± 1.02a8.75 ± 1.25aOverall results10.00 ± 0.6812.53 ± 1.36Observation group7.53 ± 1.02b6.25 ± 0.68bControl group7.54 ± 0.56a8.75 ± 0.91aCompared with the observation group,aP>0.05; compared with the control group, bP<0.05.The low individual volume rate, online abandonment rate, and complaint rate of group tourists were 6.8%, 9.0%, and 10.1%, respectively, which had no significant difference with the observation group (allP>0.05) but had a significant difference with the requirements of Jiangxi industry (all P<0.05). There are statistical differences in online abandonment rates among teams of different grades. With the increase in grades, the online abandonment rates of group tourists decrease (all P<0.05), which has no significant difference with the observation group (all P>0.05) but has a significant difference with the requirements of industries in Jiangxi (all P<0.05). The overall results are shown in Table 2.It can be seen from Figure2 that the number of tourists in the observation group is significantly higher than that in the control group, and there is an obvious difference, so the number of tourists in the observation group is larger, and the intelligent platform has better attraction effect on the number of tourists [10]. For further analysis, the satisfaction and feedback rates of different groups are compared, and the results are shown in Table 3.Figure 2
Measurement of tourist quantity in different groups.Table 3
Comparison of satisfaction among teams at all levels.
Team rankBasic satisfactionSatisfiedVery satisfiedLevel 161.25 ± 1.3677.53 ± 0.5671.25 ± 1.25Observation group56.25 ± 1.02b66.25 ± 1.25b67.5 ± 1.02bControl group63.75 ± 0.91a60.24 ± 1.13a56.25 ± 0.56aLevel 257.5 ± 0.7963.75 ± 0.6861.25 ± 0.56Observation group58.75 ± 1.36b62.5 ± 1.25b66.25 ± 1.36bControl group66.25 ± 1.02a56.25 ± 0.90a76.25 ± 0.56aLevel 377.51 ± 0.6872.53 ± 0.9281.25 ± 0.92Observation group68.75 ± 0.92b77.59 ± 1.13b71.25 ± 0.56bControl group71.25 ± 0.79a76.25 ± 0.68a71.25 ± 1.13aOverall results60.23 ± 1.1363.75 ± 0.5677.58 ± 0.93Observation group57.53 ± 1.25b65.65 ± 0.94b62.53 ± 0.94bControl group61.25 ± 1.36a81.25 ± 0.68a71.25 ± 1.23aCompared with the observation group,aP>0.05; compared with the control group, bP<0.05.Comparing the satisfaction changes among different groups, the results are shown in Figure3.Figure 3
Satisfaction changes of different groups.It can be seen from Figure3 that the satisfactory rate of the observation group changes little, which is significantly better than that of the control group. The data change range of the observation group is smaller than that of the control group, and the data nodes of the observation group are less than those of the control group, which shows that the data change of the observation group is more stable, and it also shows that the scenic big data platform can provide stable customer volume, which is superior to the traditional customer acceptance mode. In addition, the data of the observation group changed greatly at the beginning and gradually decreased at the later stage. In the middle and later period, the change range of the control group also gradually increased, which further shows that the data change of the control group is unstable. At the same time, the data indicate that the construction of the smart platform project can significantly improve the satisfaction of tourists and enhance the attractiveness of Wushu group performances [11], as shown in Table 4.Table 4
Comparison of feedback rates between teams at all levels.
VariableClass 1 groupGrade 2 groupGrade 3 groupLow individual customer volume rate (%)25.7130.3627.14Observation group28.93b28.93b17.86bControl group16.43a21.43a24.29aOnline abandonment rate (%)43.7528.7525.00Observation group28.75b30.80b18.75bControl group30.00ac27.50ac30.00aComplaint rate (%)2.032.502.50Observation group6.25b2.00b3.75bControl group1.25a5.00a3.02aRepurchase rate (%)18.7531.2518.75Observation group28.75bc25.00b26.25bControl group31.25a22.50ac27.50acBad review rate (%)7.501.251.00Observation group7.50b6.25b2.75bControl group3.75a8.75a3.10acCompared with the observation group,aP>0.05; compared with the control group, bP<0.05; compared with the observation group, cP<0.05.


## 3.3. Online Contribution Rate of Group Tourists

The average online contribution rate of tourists in 2019 year’s survey team is 52.0%, among which the contribution rate of an equal-level low-level line is 41.0%, the contribution rate of a medium-level equal-level line is 11.1%, and the contribution rate of a high-level line is 0.9%, as shown in Table5.Table 5
Online contribution rate of teams at all levels.
VariableClass 1 groupGrade 2 groupGrade 3 groupContribution rate on a low horizontal line76.5384.6981.63Observation group66.33b78.57b74.49bControl group86.73a83.67a64.29aContribution rate on a medium horizontal line82.6581.6378.57Observation group61.22b74.49b61.22bControl group68.37a85.71a78.57aContribution rate on a higher horizontal line71.4366.3381.63Observation group62.24b78.57b77.55bControl group79.59a69.39a81.63aCompared with the observation group,aP>0.05; compared with the control group, bP<0.05.It can be seen that the contribution rate on the medium level is obvious among the first-level teams, while the high level is not statistically significant among the second-level teams. Among them, in terms of low-level contribution rate, there are significant differences between grades 1 and 3; in the middle level, there are significant differences in grades 1 and 2 but no significant differences in Grade 3; and in the aspect of high-level contribution rate, there are significant differences among grades 1–3. It can be seen that there are significant differences between grades 1 and 3 in low and high contribution rates, while there is little difference between grades 1 and 3 in medium contribution rate. Therefore, the contribution rate of grades 1–3 is polarized, and the big data platform should track the tourists of the two grades and formulate different attraction schemes.The range of online ticket purchase for online transformation is particularly important for level 3 teams. And it is significantly lower than the contribution rate of 71% of domestic online tourists [12].


## 4. Discussion

The results of this study show that, compared with the observation group, the tourists who implement the smart platform are mainly Grade 2 and Grade 3 tourists, and the feedback rate and online contribution rate are higher, which is better than the effect of Grade 1 and Grade 2 groups. However, the number of individual tourists and tourists of different grades was consistent with that of the observation group [13].


### 4.1. Research on Online Transformation

The results of this study show that the contribution rates of low-level, medium-level, and high-level tourists who purchase tickets online increase first and then decrease in the first-, second-, and third-level groups, and the contribution rates of low-level, medium-level, and high-level tourists of the first- and second-level groups are higher than those of the observation group, with significant differences, but lower than those of the control group. The third-level team is the key period for scenic spots to receive tourists. There are significant differences between tourists who use online ticket purchase and tourists who do not use online ticket purchase at this stage, which shows that there is a serious problem of insufficient online conversion in Jiangxi province. At the same time, there are still differences between the online conversion tourists who use online ticket purchase and the observation group in the first-level group and the second-level group, which further shows that the online conversion problem of tourists in Jiangxi is serious [14]. The reasons for the above problems are related to the lack of system development awareness, imperfect online construction, and lack of system auxiliary platform in Jiangxi. According to the survey conducted by Jiangxi Tourism Bureau, the online contribution rate of tourists from level 3 groups exceeds 40%, which still belongs to the problem of insufficient online transformation. The research shows that the online contribution rate of tourists from the third-level groups in Jiangxi Province is 42.2%, which is higher than that of the observation group (32.4%), so “online conversion + offline ticket purchase” can be adopted. With the increase in the online conversion rate, the complaint rate and abandonment rate of tourists in level 2 and level 3 groups will decrease, indicating that online ticket purchase is beneficial to improve the online contribution rate of group tourists, which is consistent with that of the existing report conclusion [15]. The investigation and study on the mountainous areas in Northeast China, Central China, and West China also show that online ticket purchase can significantly reduce the low body mass rate of tourists [16] and that online ticket purchase can significantly improve tourists’ satisfaction and reduce the incidence of complaints. The survey of 2019 year’s tourists in Jiangxi Province shows that after correcting the regional difference value, the online contribution rate of group tourists in the survey area is 50.8%, which is significantly higher than the domestic standard of 48.2% for group tourists of grades 1–3. Therefore, building a smart platform is very important for the number of tourists in tourist areas, which can produce considerable economic benefits [17]. Therefore, it is the key to further develop targeted guidance on increasing the number of tourists.


### 4.2. The Online Abandonment Rate, Complaint Rate, and Bad Review Rate of Tourists Decreased

According to the survey results of 2019 year’s tourists in Jiangxi Province, the abandonment rate is 9%, which is lower than 9.7% of domestic online tourists, but it does not reach the goal of online abandonment rate of <5% announced by the China Tourism Administration. The results of this study show that there are significant differences between the abandonment rates of Grade 1 and Grade 2 groups and those of the observation group. The emergence of the above problems may be related to the low awareness of online ticket purchase and interest in martial arts performance of the third-level team tourists in Jiangxi Province, all of which have not reached 45%. Most group tour leaders do not know the content of Wushu, and the awareness rate is only 12.7%. Therefore, it is a problem to be solved at present to increase the propaganda of Wushu performing arts knowledge for team tourists. At the same time, the survey result shows that transportation convenience has an impact on the change of abandonment rate and complaint rate. In terms of convenience, it will affect tourists’ interest in Wushu content. According to the 2019 year’s survey in Jiangxi Province, the convenient transportation in Jiangxi Province will affect tourists’ interest in Wushu, and the complaint rate is 13.7%, which is higher than the current national average [18].


### 4.3. Impact on Bad Reviews and Repurchase Rate

The results of this study show that the bad review rate and repurchase rate of tourists who use online ticket purchase decrease significantly, which are consistent with those of the observation group but lower than those of the control group. The reason for the above problems is that Jiangxi Province is a remote area, the way of purchasing tickets is relatively simple, and its interest in martial arts performing arts is low. It mainly focuses on natural scenic spots and leisure scenic spots, especially natural scenery and cultural tourism content. Because of its special natural landscape, as well as a large number of natural landscape propaganda, there is a poor interest in Wushu tourism. However, through investigation, it is found that Jiangxi Province has a good acceptance of the construction of smart platform projects, and adopts online ticket purchase to improve the online conversion rate. According to statistics, 50.3% of the team leaders still insist on purchasing tickets online. Although they have not standardized the operation according to the requirements of the smart platform project, they can still insist on recommending to tourists. According to the survey of tourists, the overall distribution rate of 2019 year’s smart platform project construction in Jiangxi Province is 95.2%, and the overall effective online ticket purchase rate is 74.2%, which is higher than that in Guizhou, Yunnan, and Shanxi Province (81.0%), indicating that online conversion has been effectively distributed in Jiangxi.


### 4.4. Purpose of Smart Platform Project Construction

The results of this paper show that the online conversion rate, feedback rate, online contribution rate, satisfaction, individual tourists, and tourists of the smart platform projects have all improved, which shows that the smart platform is of great significance to the online conversion rate of tourists in Jiangxi. However, in this report, there are differences in the volume complaint rate and online contribution rate between the first-level group and the second-level group and the observation group. The above reasons are, on the one hand, due to the tourism environment factors in Jiangxi, which cannot be changed temporarily. On the other hand, due to the lack of awareness of online transformation, the compliance rate of online ticket purchase is not high [19]. Although the local government of Jiangxi carried out online tourism publicity and education work through the project, the tourist leader accepted online transformation and standardized and guided tourists to carry out the online transformation, and the awareness of colleagues and friends around Jiangxi tourism and martial arts performing arts will also have a certain impact on tourists. Some scholars believe that although Jiangxi Province has implemented the smart platform project, tourists’ awareness of online transformation has improved, but the online conversion rate is low, lacking online transformation awareness and understanding of martial arts performing arts. On the contrary, due to the lack of awareness of online transformation, the compliance rate of online ticket purchase is not high [20, 21].


## 4.1. Research on Online Transformation

The results of this study show that the contribution rates of low-level, medium-level, and high-level tourists who purchase tickets online increase first and then decrease in the first-, second-, and third-level groups, and the contribution rates of low-level, medium-level, and high-level tourists of the first- and second-level groups are higher than those of the observation group, with significant differences, but lower than those of the control group. The third-level team is the key period for scenic spots to receive tourists. There are significant differences between tourists who use online ticket purchase and tourists who do not use online ticket purchase at this stage, which shows that there is a serious problem of insufficient online conversion in Jiangxi province. At the same time, there are still differences between the online conversion tourists who use online ticket purchase and the observation group in the first-level group and the second-level group, which further shows that the online conversion problem of tourists in Jiangxi is serious [14]. The reasons for the above problems are related to the lack of system development awareness, imperfect online construction, and lack of system auxiliary platform in Jiangxi. According to the survey conducted by Jiangxi Tourism Bureau, the online contribution rate of tourists from level 3 groups exceeds 40%, which still belongs to the problem of insufficient online transformation. The research shows that the online contribution rate of tourists from the third-level groups in Jiangxi Province is 42.2%, which is higher than that of the observation group (32.4%), so “online conversion + offline ticket purchase” can be adopted. With the increase in the online conversion rate, the complaint rate and abandonment rate of tourists in level 2 and level 3 groups will decrease, indicating that online ticket purchase is beneficial to improve the online contribution rate of group tourists, which is consistent with that of the existing report conclusion [15]. The investigation and study on the mountainous areas in Northeast China, Central China, and West China also show that online ticket purchase can significantly reduce the low body mass rate of tourists [16] and that online ticket purchase can significantly improve tourists’ satisfaction and reduce the incidence of complaints. The survey of 2019 year’s tourists in Jiangxi Province shows that after correcting the regional difference value, the online contribution rate of group tourists in the survey area is 50.8%, which is significantly higher than the domestic standard of 48.2% for group tourists of grades 1–3. Therefore, building a smart platform is very important for the number of tourists in tourist areas, which can produce considerable economic benefits [17]. Therefore, it is the key to further develop targeted guidance on increasing the number of tourists.


## 4.2. The Online Abandonment Rate, Complaint Rate, and Bad Review Rate of Tourists Decreased

According to the survey results of 2019 year’s tourists in Jiangxi Province, the abandonment rate is 9%, which is lower than 9.7% of domestic online tourists, but it does not reach the goal of online abandonment rate of <5% announced by the China Tourism Administration. The results of this study show that there are significant differences between the abandonment rates of Grade 1 and Grade 2 groups and those of the observation group. The emergence of the above problems may be related to the low awareness of online ticket purchase and interest in martial arts performance of the third-level team tourists in Jiangxi Province, all of which have not reached 45%. Most group tour leaders do not know the content of Wushu, and the awareness rate is only 12.7%. Therefore, it is a problem to be solved at present to increase the propaganda of Wushu performing arts knowledge for team tourists. At the same time, the survey result shows that transportation convenience has an impact on the change of abandonment rate and complaint rate. In terms of convenience, it will affect tourists’ interest in Wushu content. According to the 2019 year’s survey in Jiangxi Province, the convenient transportation in Jiangxi Province will affect tourists’ interest in Wushu, and the complaint rate is 13.7%, which is higher than the current national average [18].


## 4.3. Impact on Bad Reviews and Repurchase Rate

The results of this study show that the bad review rate and repurchase rate of tourists who use online ticket purchase decrease significantly, which are consistent with those of the observation group but lower than those of the control group. The reason for the above problems is that Jiangxi Province is a remote area, the way of purchasing tickets is relatively simple, and its interest in martial arts performing arts is low. It mainly focuses on natural scenic spots and leisure scenic spots, especially natural scenery and cultural tourism content. Because of its special natural landscape, as well as a large number of natural landscape propaganda, there is a poor interest in Wushu tourism. However, through investigation, it is found that Jiangxi Province has a good acceptance of the construction of smart platform projects, and adopts online ticket purchase to improve the online conversion rate. According to statistics, 50.3% of the team leaders still insist on purchasing tickets online. Although they have not standardized the operation according to the requirements of the smart platform project, they can still insist on recommending to tourists. According to the survey of tourists, the overall distribution rate of 2019 year’s smart platform project construction in Jiangxi Province is 95.2%, and the overall effective online ticket purchase rate is 74.2%, which is higher than that in Guizhou, Yunnan, and Shanxi Province (81.0%), indicating that online conversion has been effectively distributed in Jiangxi.


## 4.4. Purpose of Smart Platform Project Construction

The results of this paper show that the online conversion rate, feedback rate, online contribution rate, satisfaction, individual tourists, and tourists of the smart platform projects have all improved, which shows that the smart platform is of great significance to the online conversion rate of tourists in Jiangxi. However, in this report, there are differences in the volume complaint rate and online contribution rate between the first-level group and the second-level group and the observation group. The above reasons are, on the one hand, due to the tourism environment factors in Jiangxi, which cannot be changed temporarily. On the other hand, due to the lack of awareness of online transformation, the compliance rate of online ticket purchase is not high [19]. Although the local government of Jiangxi carried out online tourism publicity and education work through the project, the tourist leader accepted online transformation and standardized and guided tourists to carry out the online transformation, and the awareness of colleagues and friends around Jiangxi tourism and martial arts performing arts will also have a certain impact on tourists. Some scholars believe that although Jiangxi Province has implemented the smart platform project, tourists’ awareness of online transformation has improved, but the online conversion rate is low, lacking online transformation awareness and understanding of martial arts performing arts. On the contrary, due to the lack of awareness of online transformation, the compliance rate of online ticket purchase is not high [20, 21].


## 5. Conclusion

The construction of the big data platform in scenic spots can realize online service for tourists and track tourists. The construction of the big data platform in scenic spots is not perfect. How to use the big data platform to increase the number of tourists is an urgent problem to be solved at present. Therefore, taking sports group performances as an example, this paper analyzes the construction of the big data platform in Jiangxi scenic spots and studies the relationship between the big data platform and tourists. The results show that (1) using the big data platform can improve tourists’ satisfaction and reduce tourists’ ticket abandonment rate; (2) the big data platform makes statistics on tourists’ age, income, and tourism experience [22]. It is found that young tourists and tourists with insufficient tourism experience are the main objects of using the big data platform; (3) the big data platform should provide tourism information to young tourists, low-income and martial arts enthusiasts by means of tracking and pushing, so as to attract them to travel; and (4) the big data platform can increase the number of tourists by more than 50% and reduce the ticket rejection rate by more than 9%. [23] Therefore, Jiangxi tourist attractions should increase the construction of smart platform, and promote the development of platform and the increase in the number of tourists from the perspectives of policies, schemes, and publicity. However, the research on intelligent platform in this paper is insufficient, and the investigation on tourists’ satisfaction, willingness, and personality is insufficient. Future research will analyze this aspect.



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*Source: 3562778-2022-08-18.xml*