Smart tourism information analysis and push system and method based on cloud platform

Abstract

The invention relates to the technical field of cloud computing, big data analysis and intelligent recommendation, and provides a smart tourism information analysis and push system and method based on a cloud platform, and the method comprises the steps: obtaining information data of scenic spots, carrying out the type division of the scenic spots, providing user login entrances, obtaining classification data according to the intention of a user, and carrying out the classification of the classification data. Dividing scenic spot information based on the scenic spot recommendation value; obtaining browsing data of a user; processing and analyzing to obtain a browsing interest value; analyzing and processing to obtain a priority sorting value based on the scenic spot recommendation value and the browsing interest value; and adjusting a scenic spot recommendation threshold value and the like to provide more personalized recommendation services conforming to user preferences, so that quick search is facilitated, and the experience feeling is improved.

Description

Technical Field

[0001] This invention relates to the field of tourism management technology, specifically a smart tourism information analysis and push system and method based on a cloud platform. Background Technology

[0002] With the advancement of information technology, cloud computing has become a mainstream service model. Cloud computing provides computing power, storage space, and applications as a service to users through the network, greatly reducing hardware investment costs and technical barriers. In the tourism industry, cloud platforms can provide efficient data processing capabilities, support the storage, management, and analysis of large-scale data, and lay the foundation for the construction of smart tourism systems.

[0003] A Chinese patent application with publication number CN114915654A discloses a cloud-based smart tourism information analysis and push system, relating to the tourism field. The system includes an intelligent system terminal, a data processing module, a scenic area map module, an environmental monitoring module, a scenic spot analysis module, a push module, and a smart mobile terminal. The intelligent system terminal and the smart mobile terminal are connected via CAN communication. This invention allows users to log in and input user information for analysis. Based on the user's points of interest, three corresponding scenic areas are generated for the user. The scenic area map module then constructs three-dimensional maps corresponding to these three scenic areas, projecting the user's persona onto the maps. The environmental monitoring module compares the climate of the user's location with that of the scenic area, recommending items to bring and clothing types to the user, thus greatly improving the user's travel experience and making choices more precise.

[0004] However, most tourism information service platforms on the market currently suffer from problems such as the inability to make personalized recommendations based on user preferences and scenic spot operation data in a reasonable and orderly manner, and the inability to analyze user behavior to optimize recommended content and update and categorize recommended content in a timely manner. This invention aims to solve the above problems by utilizing advanced cloud computing platforms, big data processing technology, and intelligent algorithms to provide users with more accurate and personalized tourism information services.

[0005] Therefore, the present invention provides a smart tourism information analysis and push system and method based on a cloud platform. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by this invention to solve its technical problem is: a smart tourism information analysis and push method based on a cloud platform, comprising: Acquire the tag data of the scenic area, which includes various tags for the scenic area. All tags are deduplicated and integrated into tag groups. These tag groups are sent to users for selection. The user-selected tags are marked as selected tags. Overlap analysis is performed between the scenic area's tag data and the selected tags to obtain the tag percentage of the scenic area. Acquire feedback data and visitor flow data of the scenic area within a preset collection period. Feedback data includes the number of feedback messages, and visitor flow data includes visitor volume. Based on the feedback data and visitor flow data, the attraction performance value of the scenic area is obtained. Combined with the tag percentage of the scenic area, a recommendation value for the scenic area is obtained. Based on the scenic area recommendation value, all scenic areas are divided into recommended scenic areas and non-recommended scenic areas, and the scenic area information corresponding to the recommended scenic areas is sent to users for browsing; Based on the user's browsing of recommended scenic spot information, the user's browsing data is obtained, including the ratio of browsing time to browsing frequency. The browsing data is processed and analyzed to obtain the browsing interest value. The recommendation value and browsing interest value of scenic spots are calculated and processed to obtain a priority ranking value. The recommended scenic spots are ranked according to the priority ranking value, and the ranked recommended scenic spots are pushed to users.

[0008] As a further technical solution of the present invention, the specific process for obtaining the label ratio is as follows: For any given scenic area, count the number of tags of the same type as the selected tags among the various tags of the scenic area, and mark them as the number of selected tags. Calculate the ratio of the number of selected tags to the total number of tags of all types in the scenic area to obtain the tag percentage of the scenic area, and mark it as BQ.

[0009] As a further technical solution of the present invention, the specific process for obtaining the attraction performance value is as follows: Among all feedback information corresponding to the scenic spot, the number of positive comments is counted and compared with the total number of all feedback information corresponding to the scenic spot to obtain the positive feedback rate of the scenic spot. Sum the visitor flow of all scenic spots to get the total visitor flow, and then calculate the ratio of the visitor flow of each scenic spot to the total visitor flow to get the visitor flow percentage of each scenic spot. The attraction performance value of a scenic spot is obtained by summing the positive feedback rate of the scenic spot with the proportion of visitor flow, and is labeled as XY.

[0010] As a further technical solution of the present invention, the specific process for obtaining the scenic spot recommendation value is as follows: The obtained scenic area label percentage (BQ) and attraction performance value (XY) are processed using the formula: The scenic area received a recommendation value of TJ, among which, and All are preset correlation coefficients.

[0011] As a further technical solution of the present invention, the specific process of dividing and sorting all scenic area information based on scenic area recommendation values ​​is as follows: If the scenic area's recommended value is greater than or equal to the scenic area's recommended threshold If the information is incorrect, it will be marked as a recommended scenic spot, and the corresponding scenic spot information will be marked as recommended scenic spot information and recommended to the user. If the recommended value of a scenic spot is less than the recommended threshold for a scenic spot, If it is marked as a non-recommended scenic spot, no action will be taken.

[0012] As a further technical solution of the present invention, the process for obtaining the browsing duration ratio and browsing frequency ratio is as follows: Based on any recommended scenic spot information, obtain the user's browsing time for the recommended scenic spot information, and process the ratio of the browsing time to the user's total browsing time to obtain the browsing time ratio SJ; Total browsing time is the total time a user spends browsing all recommended scenic spot information. Based on any recommended scenic spot information, obtain the number of times the user views the recommended scenic spot information, and process the ratio of the number of views to the total number of views of the user to obtain the view count ratio CS; Total number of views is the number of times a user views all recommended scenic spot information.

[0013] As a further technical solution of the present invention, the specific process for obtaining the browsing interest value is as follows: The obtained browsing duration ratio (SJ) and browsing frequency ratio (CS) are processed using the formula: The browsing interest value XQ is obtained, where, and All are preset proportional coefficients.

[0014] As a further technical solution of the present invention, the specific process for obtaining the priority ranking value is as follows: The obtained scenic spot recommendation value TJ and browsing interest value XQ are processed using the formula: The priority ranking value PX is obtained, where, and All are preset proportional coefficients.

[0015] As a further technical solution of the present invention, the specific process of optimizing, analyzing, ranking, and recommending users is as follows: Based on the calculated priority ranking value PX, the recommended scenic spots are sorted from largest to smallest according to the priority ranking value PX and pushed to users in sequence.

[0016] A cloud-based smart tourism information analysis and push system includes: Scenic spot label processing module: acquires the label data of scenic spots, sends the label group to the user for selection, and performs overlap analysis between the label data of scenic spots and the selected labels to obtain the label ratio of scenic spots; Scenic Spot Recommendation Value Processing Module: Obtains feedback data and visitor flow data of scenic spots within a preset collection period. Based on the feedback data and visitor flow data, it processes the data to obtain the attraction performance value of the scenic spot and combines it with the proportion of the scenic spot's tags to obtain the scenic spot recommendation value. Recommended scenic area classification module: Based on the scenic area recommendation value, all scenic areas are classified into recommended scenic areas and non-recommended scenic areas; Recommended scenic spot push module: Sends information about recommended scenic spots to users for browsing; User data analysis module: Based on the user's browsing data after browsing recommended scenic spot information, the module obtains the user's browsing data, processes and analyzes the browsing data to obtain the browsing interest value, and calculates and processes the scenic spot recommendation value and browsing interest value to obtain the priority ranking value. Recommended scenic spots sorting module: Sorts recommended scenic spots according to priority values ​​and pushes the sorted recommended scenic spots to users; Cloud platform: A software and service platform built on cloud computing technology that collects scenic area and user information, provides user login access, and analyzes and processes data.

[0017] The beneficial effects of this invention are as follows: 1. Acquire the tag data of the scenic area, which includes various tags of the scenic area. All tags of the scenic area are deduplicated and integrated into tag groups. The tag groups are sent to the user for selection. The user-selected tags are marked as selected tags. The tag data of the scenic area is analyzed for overlap with the selected tags to obtain the tag ratio of the scenic area. 2. Acquire the feedback data and visitor flow data of the scenic area within a preset collection period. The feedback data includes the number of feedback messages, and the visitor flow data includes visitor volume. Based on the feedback data and visitor flow data, the attraction performance value of the scenic area is obtained. Combined with the tag ratio of the scenic area, a recommendation value for the scenic area is obtained. This invention, through the division of scenic area information, based on user needs, and in a reasonable and orderly manner, provides personalized recommendations according to user preferences and scenic area operation data, effectively improving the user experience.

[0018] 2. Based on users browsing recommended scenic spot information, the system acquires user browsing data, including the ratio of browsing time to browsing frequency. This data is processed and analyzed to obtain a browsing interest value. The system then calculates and processes the scenic spot recommendation value and browsing interest value to obtain a priority ranking value. Recommended scenic spots are ranked according to this priority ranking value, and the ranked recommendations are pushed to users, making the recommendation results more objective and fair. This system is not only applicable to scenic spot recommendations but can also be applied to product or service recommendations in other fields, exhibiting high scalability. Attached Figure Description

[0019] The invention will now be further described with reference to the accompanying drawings.

[0020] Figure 1 This is a flowchart illustrating the steps of a cloud-based smart tourism information analysis and push method according to an embodiment of the present invention. Figure 2 This is a flowchart of a cloud-based smart tourism information analysis and push system according to an embodiment of the present invention. Detailed Implementation

[0021] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments. Example 1

[0022] Please see Figure 1 As shown in the embodiment of the present invention, a smart tourism information analysis and push method based on a cloud platform includes: Step 1: Obtain the scenic area's tag data, which includes various tags for the scenic area. All tags are deduplicated and consolidated into tag groups. These tag groups are sent to users for selection. The user-selected tags are marked as selected tags. The scenic area's tag data is then analyzed for overlap with the selected tags to obtain the tag percentage of the scenic area. Next, obtain the scenic area's feedback data and visitor flow data within a preset collection period. Feedback data includes the number of feedback messages, and visitor flow data includes visitor volume. Based on the feedback data and visitor flow data, the scenic area's attractiveness performance value is obtained. Combined with the scenic area's tag percentage, a recommended value for the scenic area is then obtained. It should be noted that the various labels for scenic spots include, but are not limited to, labels for cultural relics, scenic spots and historical sites, and labels for natural scenery. For example, the method for obtaining the percentage of tags for scenic spots is as follows: For any given scenic area, count the number of tags of the same type as the selected tags among the various tags of the scenic area, and mark them as the number of selected tags. Calculate the ratio of the number of selected tags to the total number of all types of tags in the scenic area to obtain the tag percentage of the scenic area, and mark it as BQ. For example, the attraction performance value of a scenic spot is obtained as follows: Among all feedback information corresponding to the scenic spot, the number of positive comments is counted and compared with the total number of all feedback information corresponding to the scenic spot to obtain the positive feedback rate of the scenic spot. Sum the visitor flow of all scenic spots to get the total visitor flow, and then calculate the ratio of the visitor flow of each scenic spot to the total visitor flow to get the visitor flow percentage of each scenic spot. The attraction performance value of the scenic spot is obtained by summing the positive feedback rate of the scenic spot with the proportion of the scenic spot’s visitor flow, and is denoted as XY. The obtained scenic area label percentage (BQ) and attraction performance value (XY) are processed using the formula: The scenic area received a recommendation value of TJ, among which, and All are preset correlation coefficients; It should be noted that the scenic spot recommendation value TJ means: the scenic spot recommendation value TJ is calculated by the tag proportion BQ and the attraction performance value XY. The larger the tag proportion, the more the type of scenic spot matches the user's expectations and the better it meets the user's needs. Similarly, the larger the attraction performance value, the more popular the scenic spot is and the higher the likelihood that the user will accept it. The tag proportion BQ and attraction performance value XY reflect the key information of the scenic spot as a whole. That is, the larger the scenic spot recommendation value TJ, the more the scenic spot matches the user's needs and should be recommended to the user first. Conversely, the smaller the scenic spot recommendation value TJ, the less the scenic spot matches the user's needs and should be recommended later. Step 2: Based on the scenic spot recommendation value, all scenic spots are divided into recommended scenic spots and non-recommended scenic spots, and the scenic spot information corresponding to the recommended scenic spots is sent to users for browsing; In some embodiments, the recommended value of a scenic spot is compared with the recommended threshold of a scenic spot. The specific comparison process is as follows: If the scenic area's recommended value is greater than or equal to the scenic area's recommended threshold If the information is incorrect, it will be marked as a recommended scenic spot, and the corresponding scenic spot information will be marked as recommended scenic spot information and recommended to the user. If the scenic area's recommended value is less than the scenic area's recommended threshold If it is marked as a non-recommended scenic spot, no action will be taken. The technical solution of this invention is as follows: Acquire tag data of scenic spots, including various tags for each scenic spot; deduplicate and integrate all tags into tag groups; send the tag groups to the user for selection; mark the user-selected tags as selected tags; perform overlap analysis between the scenic spot's tag data and the selected tags to obtain the tag percentage of the scenic spot; acquire feedback data and visitor flow data of the scenic spot within a preset collection period, including the number of feedback messages and visitor flow data including visitor volume; based on the feedback data and visitor flow data, process to obtain the scenic spot's attractiveness performance value; combine this with the tag percentage of the scenic spot to obtain a recommended value; classify all scenic spots based on the recommended value, dividing them into recommended and non-recommended scenic spots; and send the information of the recommended scenic spots to the user for browsing. This invention, through the classification of scenic spot information, based on user needs, and rationally and orderly makes personalized recommendations according to user preferences and scenic spot operation data, effectively improving the user experience. Example 2

[0023] Based on Example 1, Figure 1 As shown in the embodiment of the present invention, a smart tourism information analysis and push method based on a cloud platform includes: Step 3: Based on the user's browsing of recommended scenic spot information, obtain the user's browsing data, including the ratio of browsing time to browsing frequency. Process and analyze the browsing data to obtain the browsing interest value. Calculate and process the scenic spot recommendation value and browsing interest value to obtain the priority ranking value. Sort the recommended scenic spots according to the priority ranking value and push the sorted recommended scenic spots to the user. In some embodiments, information is based on any recommended scenic spot; The browsing time of users on recommended scenic spot information is obtained, and the browsing time is compared with the user's total browsing time to obtain the browsing time ratio SJ. Total browsing time is the total time a user spends browsing all recommended scenic spot information. Based on any recommended scenic spot information, obtain the number of times the user views the recommended scenic spot information, and process the ratio of the number of views to the total number of views of the user to obtain the view count ratio CS; Total number of views is the number of times a user views all recommended scenic spot information; The obtained browsing duration ratio (SJ) and browsing frequency ratio (CS) are processed using the formula: The browsing interest value XQ is obtained, where, and All are preset proportional coefficients; It should be noted that the browsing interest value XQ is calculated by analyzing the ratio of browsing time to browsing frequency. The higher the ratio of browsing time to browsing frequency, the greater the user's interest, which makes it easier to analyze and process user needs and improve user satisfaction. The obtained scenic spot recommendation value TJ and browsing interest value XQ are processed using the formula: The priority ranking value PX is obtained, where, and All are preset proportional coefficients; It should be noted that the priority ranking value PX is calculated using the scenic spot recommendation value TJ and the browsing interest value XQ. It reflects the recommended scenic spots that better meet the user's needs. The scenic spot recommendation value TJ reflects the degree to which the tags included in the scenic spot match the tags selected by the user, as well as the popularity of the scenic spot. The higher the scenic spot recommendation value TJ, the better the scenic spot meets the user's needs. The browsing interest value XQ reflects the user's interest in the recommended scenic spot. The higher the browsing interest value XQ, the more interested the user is in the recommended scenic spot. Based on the calculated priority ranking value PX, the recommended scenic spots are sorted from largest to smallest according to the priority ranking value PX and pushed to users in sequence. The technical solution of this invention is as follows: Based on the user's browsing of recommended scenic spot information, the user's browsing data is obtained, including the ratio of browsing time to browsing frequency. The browsing data is processed and analyzed to obtain a browsing interest value. The scenic spot recommendation value and the browsing interest value are calculated and processed to obtain a priority ranking value. The recommended scenic spots are ranked according to the priority ranking value, and the ranked recommended scenic spots are pushed to the user, making the recommendation results more objective and fair. It is not only applicable to scenic spot recommendations, but can also be applied to product or service recommendations in other fields, and has high scalability. Example 3

[0024] like Figure 2 As shown in the embodiment of the present invention, a smart tourism information analysis and push system based on a cloud platform includes: Scenic spot label processing module: acquires the label data of scenic spots, sends the label group to the user for selection, and performs overlap analysis between the label data of scenic spots and the selected labels to obtain the label ratio of scenic spots; Scenic Spot Recommendation Value Processing Module: Obtains feedback data and visitor flow data of scenic spots within a preset collection period. Based on the feedback data and visitor flow data, it processes the data to obtain the attraction performance value of the scenic spot and combines it with the proportion of the scenic spot's tags to obtain the scenic spot recommendation value. Recommended scenic area classification module: Based on the scenic area recommendation value, all scenic areas are classified into recommended scenic areas and non-recommended scenic areas; Recommended scenic spot push module: Sends information about recommended scenic spots to users for browsing; User data analysis module: Based on the user's browsing data after browsing recommended scenic spot information, the module obtains the user's browsing data, processes and analyzes the browsing data to obtain the browsing interest value, and calculates and processes the scenic spot recommendation value and browsing interest value to obtain the priority ranking value. Recommended scenic spots sorting module: Sorts recommended scenic spots according to priority values ​​and pushes the sorted recommended scenic spots to users; Cloud platform: A software and service platform built on cloud computing technology that collects scenic area and user information, provides user login access, and analyzes and processes data.

[0025] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention and should not be considered as limiting the scope of the invention. All equivalent variations and improvements made within the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1.A cloud platform-based smart tourism information analysis and push method, characterized in that: The method comprises the following steps: Obtaining label data of the scenic spots, wherein the label data comprises various types of labels of the scenic spots, the various types of labels of all the scenic spots are de-duplicated and integrated into a label group, the label group is sent to a user for selection, the selected label of the user is marked as a selected label, the label data of the scenic spots is overlapped with the selected label, and a label proportion of the scenic spots is obtained through analysis; Obtaining feedback data and passenger flow data of the scenic spots within a preset collection period, processing the feedback data and the passenger flow data to obtain an attraction performance value of the scenic spots, and combining the label proportion of the scenic spots to obtain a scenic spot recommendation value; Dividing all the scenic spots based on the scenic spot recommendation value, dividing the scenic spots into recommended scenic spots and non-recommended scenic spots, and sending the information of the recommended scenic spots to a user for browsing; After the user browses the information of the recommended scenic spots, obtaining browsing data of the user, wherein the browsing data comprises a browsing time ratio and a browsing frequency ratio, processing and analyzing the browsing data to obtain a browsing interest value; Processing and calculating the scenic spot recommendation value and the browsing interest value to obtain a priority sorting value, sorting the recommended scenic spots according to the priority sorting value, and pushing the sorted recommended scenic spots to the user. 2.The cloud platform-based smart tourism information analysis and push method according to claim 1, characterized in that: The specific process of obtaining the label proportion comprises the following steps: Based on any one scenic spot, the number of labels of the same type as the selected label is counted from the various types of labels of the scenic spot, and the number is marked as a selected label number; the selected label number is processed by ratio with the total number of various types of labels of the scenic spot to obtain the label proportion of the scenic spot, and the label proportion is marked as BQ. 3.The cloud platform-based smart tourism information analysis and push method according to claim 1, characterized in that: The specific process of obtaining the attraction performance value comprises the following steps: In all the feedback information corresponding to the scenic spot, the number of positive comment information is counted, and ratio processing is performed between the number of positive comment information and the total number of all the feedback information corresponding to the scenic spot to obtain a positive feedback rate of the scenic spot; The passenger flow of all the scenic spots is summed to obtain a total passenger flow, and ratio processing is performed between the passenger flow of the scenic spot and the total passenger flow to obtain a passenger flow proportion of the scenic spot; The positive feedback rate of the scenic spot and the passenger flow proportion of the scenic spot are summed to obtain the attraction performance value of the scenic spot, and the attraction performance value is marked as XY. 4.The cloud platform-based smart tourism information analysis and push method according to claim 1, characterized in that: The specific process of obtaining the scenic spot recommendation value comprises the following steps: The label proportion BQ of the obtained scenic spot and the attraction performance value XY of the scenic spot are subjected to data processing, and a scenic spot recommendation value TJ is obtained through a formula: wherein, and are both preset correlation coefficients. 5.The cloud platform-based smart tourism information analysis and push method according to claim 1, characterized in that: The specific process of dividing and sorting all the scenic spot information based on the scenic spot recommendation value comprises the following steps: If the scenic spot recommendation value is greater than or equal to the scenic spot recommendation threshold value , the scenic spot is marked as a recommended scenic spot, the scenic spot information corresponding to the recommended scenic spot is marked as recommended scenic spot information, and the user is recommended. If the scenic spot recommendation value is less than the scenic spot recommendation threshold value , it is marked as a non-recommended scenic spot, and no operation is performed. 6.The cloud platform-based smart tourism information analysis and push method according to claim 1, characterized in that: The specific process of obtaining the browsing time ratio and the browsing frequency ratio comprises the following steps: Based on any one recommended scenic spot information, the browsing time of the user for the recommended scenic spot information is obtained, the browsing time is processed by ratio with the total browsing time of the user to obtain a browsing time ratio SJ; The total browsing time is the time of the user browsing all the recommended scenic spot information; Based on any one recommended scenic spot information, the browsing frequency of the user for the recommended scenic spot information is obtained, the browsing frequency is processed by ratio with the total browsing frequency of the user to obtain a browsing frequency ratio CS; The total browsing frequency is the number of times of the user browsing all the recommended scenic spot information. 7.The cloud platform-based smart tourism information analysis and push method according to claim 1, characterized in that: The specific process of obtaining the browsing interest value comprises the following steps: The obtained browsing time length ratio SJ and the browsing times ratio CS are processed, and the browsing interest value XQ is obtained through the formula: wherein, and are preset proportion coefficients. 8.The cloud platform-based smart tourism information analysis and push method of claim 7, wherein: The specific process of obtaining the priority sorting value comprises the following steps: The obtained scenic spot recommendation value TJ and the browsing interest value XQ are subjected to data processing, and a priority sorting value PX is obtained through a formula: wherein, a and b are preset proportion coefficients. and are preset proportion coefficients. 9.The cloud platform-based smart tourism information analysis and push method of claim 8, wherein: The specific process of the optimization analysis sorting and recommending to the user comprises the following steps: Based on the calculated priority sorting value PX, the recommended scenic spots are sorted according to the priority sorting value PX from large to small, and are sequentially pushed to the user. 10.A cloud platform-based smart tourism information analysis and push system, which is used to implement the smart tourism information analysis and push method according to any one of claims 1-9, characterized in that: The method comprises the following steps: The scenic spot label processing module: obtains label data of the scenic spot, sends a label group to the user for selection, and performs coincidence analysis on the label data of the scenic spot and the selected label to obtain a label proportion of the scenic spot; The scenic spot recommendation value processing module: obtains feedback data and passenger flow data of the scenic spot within a preset collection period, processes an attraction performance value of the scenic spot based on the feedback data and the passenger flow data, and processes a scenic spot recommendation value in combination with the label proportion of the scenic spot; The recommended scenic spot division module: divides all scenic spots based on the scenic spot recommendation value, and divides the scenic spots into recommended scenic spots and non-recommended scenic spots; The recommended scenic spot pushing module: sends scenic spot information corresponding to the recommended scenic spot to the user for browsing; The user data analysis module: obtains browsing data of the user based on browsing of the recommended scenic spot information by the user, processes and analyzes the browsing data to obtain a browsing interest value, calculates and processes the scenic spot recommendation value and the browsing interest value to obtain a priority sorting value; The recommended scenic spot sorting module: sorts the recommended scenic spots according to the priority sorting value, and pushes the sorted recommended scenic spots to the user; The cloud platform: a software and service platform constructed based on cloud computing technology, which collects scenic spot and user information, provides a user login portal, and analyzes and processes data.

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