Method for dividing and screening urban tourism data

Abstract

The invention discloses a method for dividing and screening urban tourism data, which comprises the following steps: S1, collecting entities with coordinate attributes and tourism values in a city to form tourism data, and carrying out regionalized spatial semantic modeling on the tourism data; and S2, dividing the tourism data into a plurality of tourism areas, and the like. According to the method, through regionalized spatial semantic modeling and accurate spatial definition of tourism POI data, scattered scenic spots, catering and accommodation data are converted into a'regionalized resource cluster ', and the'regionalized resource cluster' is visually presented through a tourism map. According to the method, tourists can quickly establish an urban tourism area framework, directly position high-value resources in combination with the scenic spot score sequence table, and do not need to depend on fuzzy administrative region or business district information, so that the travel planning time is effectively shortened, the decision-making pain point that regional distribution is not known and resources are difficult to select in a traditional scene is solved, and the decision-making precision and efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of urban tourism data screening, in particular to a method for dividing and screening urban tourism data. BACKGROUND

[0002] In recent years, with the upgrading of the demand for precise and efficient tourism consumption, the demand for structured presentation and efficient retrieval of spatial dimension information in the tourism decision-making process has significantly increased. However, the current tourism service system has technical shortcomings in the aspects of regional modeling and retrieval logic optimization of urban tourism resources, resulting in a double bottleneck of spatial cognition and decision-making efficiency for users.

[0003] From the perspective of user demand-side technical pain points, the existing system does not construct a regional spatial semantic model of urban tourism resources, and cannot provide structured tourism regional division information for users. Taking the Chengdu tourism scene as an example, users can only obtain discrete point of interest (POI) data, lacking regional semantic description of areas with clear geographical boundaries and resource association characteristics such as "urban core tourism functional area" and "West Dujiangyan-Qingcheng Mountain tourism functional cluster", which makes it difficult for users to establish spatial topological relationship cognition of tourism resources, and further makes it difficult to complete decision-making behaviors such as cross-regional transportation cost calculation and travel time planning, increasing the time complexity and path planning risk of user decision-making.

[0004] From the perspective of system supply-side technical defects, the information architecture and retrieval engine of mainstream tourism service platforms have two core technical deficiencies: first, the resource integration layer does not realize structured modeling of tourism regional dimension, and does not associate and map multi-type POI data such as scenic spots, catering, and accommodation with tourism regional spatial boundaries, which cannot form regional resource data sets; second, the retrieval logic layer does not construct a retrieval index centered on tourism regions, and the existing retrieval engine only supports traditional logic such as "commercial district POI association retrieval", "administrative region boundary retrieval", and "single POI precise retrieval", without designing retrieval entry and filtering algorithms based on tourism regional semantics, resulting in long retrieval links and retrieval efficiency that cannot meet the needs of efficient decision-making of users.

[0005] In summary, the current tourism service system has technical contradictions of "user spatial cognition semantic deficiency" and "system regional modeling and retrieval capability deficiency", and it is urgent to construct a technical solution with tourism regional spatial semantic modeling capability and regional retrieval function. SUMMARY

[0006] The present application aims to overcome the technical contradictions of "user spatial cognition semantic deficiency" and "system regional modeling and retrieval capability deficiency" in the current tourism service system, and provides a method for dividing and screening urban tourism data.

[0007] The objective of this invention is achieved through the following technical solution: a method for classifying and filtering urban tourism data, comprising the following steps: S1. Collect entities with coordinate attributes and tourism value in the city to form tourism data, and perform regional spatial semantic modeling on the tourism data. S2. Divide the tourism data into a number of tourism regions and establish a mapping relationship with administrative regions; S3. Map the tourism data within each tourism area and between different tourism areas to form a tourism map; S4. Users filter based on the travel map, and the travel map displays a list of corresponding travel data according to the selection.

[0008] Furthermore, the "entities with coordinate attributes and tourism value" mentioned in step S1 refers to tourism POI data with geospatial identifiers.

[0009] The tourism POI data refers to point-like information data with specific tourism-related attributes and significance in geographic space, which includes at least basic geographic information, attribute description information, evaluation and rating information, and business information.

[0010] Furthermore, the "generating tourism data" mentioned in step S1 specifically includes the following steps: S11. Statistics on the distribution of tourist areas and attractions in the city; S12, Mark the highest-scoring attractions in each tourist area; S13, Mark the snack streets in each tourist area; S14. Mark the accommodation distribution in each tourist area; S15. Generate tourism data.

[0011] The "regional spatial semantic modeling of the tourism data" mentioned in step S1 includes at least location association, semantic labels, and rule mapping relationships; the location association includes at least basic spatial anchoring and tourism area boundary association; the semantic labels include attraction type, service type, and tourist reviews; the rule mapping relationships include intra-regional mapping relationships and inter-regional mapping relationships.

[0012] Furthermore, step S2, "dividing tourism data into a number of tourism areas and establishing a mapping relationship with administrative regions," specifically includes the following steps: S21. Generate a tourism area segmentation threshold based on the attraction scores under the city. S22, Generate scores for attractions within the city. A list of attractions with tourism area segmentation thresholds; S23. For each attraction in the attraction list, draw a circle with a radius of 10-50km centered on the attraction's coordinates, forming a number of attraction coordinate circles; S24. Determine whether the coordinate circles of each scenic spot intersect. If they intersect, merge the coordinate circles of that scenic spot; if they do not intersect, leave them unchanged. S25. Generate a number of scenic spot ranges and map the administrative region where each scenic spot range is located; if the administrative regions overlap, merge them into one region and designate that region as a tourist area; if they do not overlap, leave them unchanged and designate each scenic spot range as a tourist area.

[0013] The "mapping of tourism data within each tourism area and between tourism areas" mentioned in step S3 refers to using spatial computing and database association technologies to aggregate and transform data within and between tourism areas into a visualized and interactive tourism spatial data model.

[0014] The tourism data list mentioned in step S4 includes at least a list of attraction score sequences and a list of tourism resources.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: (1) This invention transforms scattered scenic spot, catering, and accommodation data into a “regional resource cluster” through regionalized spatial semantic modeling and precise spatial definition of tourism POI data, and presents it through tourism map visualization. Tourists can quickly establish a “city tourism area framework” and directly locate high-value resources by combining the scenic spot score sequence table, without having to rely on vague administrative district or business district information, effectively shortening the trip planning time, solving the decision-making pain point of “not knowing the regional distribution and having difficulty selecting resources” in traditional scenarios, and improving the accuracy and efficiency of decision-making.

[0016] (2) This invention uses tourism areas as the core filtering dimension. Through the mapping of tourism data within the area and the visualization of relationships between areas, it supports users to directly trigger filtering based on tourism areas (such as clicking on a tourism area to get a full list of attractions, restaurants and accommodations within the area), replacing the cumbersome process of traditional "multi-level filtering + manual filtering", greatly improving the accuracy and response speed of resource retrieval. It not only fills the functional gap of "tourism area dimension filtering" in the industry, but also optimizes the tourism resource filtering logic and breaks through the bottleneck of low retrieval efficiency of traditional platforms.

[0017] (3) This invention transforms traditional fragmented tourism data into “regionalized, structured, and associative” data assets through a full-process design of “scenic spot score calculation, regional segmentation threshold generation, and multi-dimensional POI data binding”. It can not only provide users with three-dimensional information of “score sorting + resource matching” (such as the highest-scoring scenic spot in a certain area and surrounding snack streets and accommodations), but also provide data support for platform operation of “regional popularity analysis and resource matching gap identification”, solving the problem of “fragmented and difficult to reuse” traditional data and improving the value of data utilization.

[0018] (4) This invention uses “inter-regional mapping” (spatial calculation of distance and mode of transportation between regions, and database binding of passenger flow linkage) to visualize the inter-regional relationship in the tourism map, and combines it with the resource list in the region to help users efficiently plan cross-regional trips, avoid problems such as unreasonable routes and wasted time caused by “inter-regional information fragmentation”, and improve the cross-regional tourism experience.

[0019] (5) The tourism area division logic of the present invention is universal. Whether it is a large city with dense tourism resources or a small and medium-sized city with scattered resources, it can adapt to the local resource distribution characteristics by adjusting the "regional segmentation threshold" and "coordinate circle radius". At the same time, the multi-dimensional modeling of POI data can meet the needs of subsequent tourism consumption upgrades, solve the problem of "poor adaptability and difficulty in expansion" of traditional methods, and provide technical support for the large-scale promotion and functional iteration of tourism service platforms. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0021] Figure 2 This is a schematic diagram of the process of tourism data generation in this invention.

[0022] Figure 3 This is a schematic diagram illustrating the process of generating tourist areas and establishing a mapping relationship with administrative regions according to the present invention. Detailed Implementation

[0023] The present invention will be further described in detail below with reference to embodiments, but the implementation of the present invention is not limited thereto.

[0024] Example

[0025] like Figure 1 As shown, the urban tourism data segmentation and filtering method described in this embodiment mainly includes four steps, S1 to S4. Step S1 involves collecting entities with coordinate attributes and tourism value within the city to form tourism data, and then performing regionalized spatial semantic modeling on this tourism data.

[0026] This step is fundamental to solving the spatial organization and efficient utilization of tourism information. Its main purpose is to build a standardized tourism data foundation and solve the problem of data fragmentation. The entities with coordinate attributes and tourism value refer to tourism POI data with geospatial identifiers, including attractions, hotels, restaurants, shopping venues, etc. This tourism POI data refers to point-like information data with specific tourism-related attributes and significance in geographic space, which includes at least basic geographic information, attribute description information, evaluation and rating information, and operational information.

[0027] The basic geographic information mentioned above is the core data for locating the spatial position of POI, which specifically includes: coordinate information (such as longitude and latitude) and altitude (for mountainous scenic areas, plateau attractions, etc.); address information, such as standardized administrative addresses or surrounding landmarks; spatial association information, such as the administrative region to which it belongs, adjacent tourist areas, and distance to transportation hubs.

[0028] The attribute description information defines what a POI is and what its characteristics are. Its purpose is to differentiate the type, function, and core features of tourism entities, helping users quickly determine whether it meets their needs. Specifically, it includes: type attributes, resource characteristic attributes, and supporting facility attributes. Among these, the type attributes are divided into core categories and subcategories.

[0029] The distinctive attributes of a resource include its natural / cultural features and core experience projects. Natural / cultural features refer to whether the entity has natural or cultural characteristics, such as being a "5A-level scenic spot," a "World Cultural Heritage site," a "National Geopark," or a "Ming and Qing Dynasty Imperial Garden." Core experience projects refer to features such as "accessible for hiking," "offering boat services," "including immersive performances," and "supporting parent-child craft experiences."

[0030] The supporting facilities attribute refers to whether it includes parking lots, restrooms, accessible pathways, visitor service centers, mother and baby rooms, restaurants, souvenir shops, etc.

[0031] The evaluation and rating information is feedback data used to reflect the "quality of user experience" of POIs. Its purpose is to quantify the reputation and quality of tourism entities and provide objective reference for users to select options. Specifically, it includes rating data, evaluation content, and reputation data, etc.

[0032] The operational information refers to practical data on how users "use this POI," aiming to solve real-world problems such as "when to go, how to make reservations, and how much it costs," thereby reducing users' travel decision-making costs. Specifically, it includes: time information, cost information, and reservation and service information. Time information includes the attraction's opening hours, adjustments during special periods (such as extended opening hours on holidays and shortened opening hours during off-seasons), and the best time to visit (such as "cherry blossom season in spring (March-April)" and "autumn foliage season in autumn (October-November)"). Cost information includes ticket / consumption prices and preferential policies. Reservation and service information includes reservation methods (whether advance reservations are required, reservation platforms <official WeChat account / third-party APP>, reservation quota limits), service contact information (official inquiry phone number, customer service WeChat, emergency rescue phone number), and operational status (such as "open as usual," "temporarily closed for maintenance," and "seasonally closed <such as ski resorts closing in winter and summer>).

[0033] After collecting the above information, it needs to be converted into tourism data according to unified standards or requirements. The specific process is as follows: Figure 2 As shown, it includes the following steps: S11. Analyze the distribution of tourist areas and attractions in the city. This step requires the use of technologies such as Geographic Information Systems (GIS), big data analytics, and database management, and includes data collection, processing, and analysis.

[0034] S12, Mark the highest-scoring attractions in each tourist area.

[0035] This step, within the city tourism data segmentation and screening process, involves comparing data and determining rules for each designated tourism area to identify the attractions with the highest tourist ratings and overall scores within that area, and then clearly marking them.

[0036] The highest score is the "comprehensive score" calculated based on the evaluation and rating information in the tourism POI data, combined with multi-dimensional indicators. Common judgment dimensions include, but are not limited to: direct user ratings, such as star ratings (e.g., 1-5 stars) given by tourists on OTA platforms (e.g., Ctrip, Meituan) and social platforms (e.g., Dianping, Xiaohongshu) for attractions, and average scores.

[0037] Evaluation content derived score: By analyzing tourist reviews through natural language processing (NLP), the "positive review rate" and "negative review percentage" are extracted and converted into a quantitative score (e.g., a positive review rate of 90% corresponds to 4.5 points).

[0038] Authoritative ratings: These ratings are based on assessments of attractions by government cultural and tourism departments and industry associations (such as 5A / 4A level scenic spots and cultural heritage protection units). Weighted scores are assigned according to the rating (e.g., a 5A scenic spot has a default base score of 4.8 points, and a 4A scenic spot has 4.2 points).

[0039] Additional weighted dimensions: The basic score is fine-tuned by combining the attraction's "popularity data" (such as annual visitor volume and search volume) and "service completeness" (such as parking lot and accessibility facilities coverage) to ensure that the score is more in line with the actual experience of tourists.

[0040] The aforementioned indicators can be weighted according to preset weights to calculate a comprehensive score. For example, the weight of direct user ratings can be set to 40%, the weight of scores derived from evaluation content to 25%, the weight of authoritative institution ratings to 25%, and the weight of additional dimensions to 10%. The weights of each indicator can be adjusted according to platform rules.

[0041] The term "labeling" refers to deeply associating the "highest-scoring scenic spot" with the spatial and attribute data of the tourist area, providing support for subsequent steps (such as regional mapping and tourist map generation), specifically including: Spatial coordinate labeling, in a GIS system, involves binding the latitude and longitude coordinates of the highest-scoring scenic spot to the boundary of the tourist area to which it belongs, thus clarifying the spatial affiliation of "this scenic spot belongs to a certain area"; Attribute tagging adds exclusive tags to the highest-rated attractions, such as "TOP1 in XX area", "5A scenic spot" and "visitor rating 4.9", and links them to their basic information (such as address and opening hours) and evaluation summary (such as "must-see sunset viewing platform"). Data association markers are used to create a "tourist area - highest-scoring attraction" association field in the database. For example, "West Lake Tourist Area" corresponds to "West Lake Scenic Area (rating 4.9)". This association can be quickly invoked when generating tourist maps or filter lists.

[0042] S13. Mark the food streets in each tourist area. This step is based on the designated tourist area and selects concentrated streets in the area with distinctive catering and local snacks as the core business (such as the food street in the tourist area where Chengdu Jinli is located, and the food street in the tourist area where Xi'an Muslim Quarter is located). By binding the street coordinates, core snack categories, operating hours and other attribute information, the spatial and attribute marking of the food streets in the area is completed, and the core carrier of catering services in each tourist area is clarified.

[0043] S14. Mark the accommodation distribution in each tourist area. This step involves integrating POI data for hotels, guesthouses, inns, and other accommodations within each tourist area. By linking accommodation coordinates, room types, price ranges, user ratings, and other information, the spatial distribution of accommodation resources within the area is marked, clearly presenting the accommodation supply and distribution density of each tourist area.

[0044] S15. Generate tourism data. This step involves structurally integrating the food streets marked in S13 and the accommodation distribution marked in S14 with the previously completed data such as "tourism area division, marking of the highest-rated attractions within the area, and basic geographic and evaluation information of POIs" to form a comprehensive and interconnected tourism dataset covering "regional boundaries, core attractions, distinctive restaurants, and accommodation facilities." This provides complete data support for subsequent regional spatial semantic modeling and tourism map generation.

[0045] After collecting tourism data by identifying entities with coordinate attributes and tourism value in the city in step S1, it is necessary to perform regional spatial semantic modeling on the tourism data.

[0046] The so-called regionalized spatial semantic modeling of the tourism data is a process of transforming scattered tourism data (such as scenic spots, food streets, accommodations, etc.) into a structured model with regional spatial attributes and semantic logic by constructing a three-dimensional association system of "location-semantics-rules". This modeling includes at least location association, semantic labels and rule mapping relationships.

[0047] The location association includes at least basic spatial anchoring and tourism area boundary association. Specifically, it anchors the spatial coordinates of data with "location association," and through "basic spatial anchoring" (binding entities such as scenic spots, food streets, and accommodations to specific geographical coordinates such as latitude, longitude, and address) and "tourism area boundary association" (clarifying the tourism area to which each entity belongs, such as a homestay belonging to the "West Lake Tourism Area" rather than the "Xixi Wetland Tourism Area"), all tourism data is attached to a clear regional spatial framework, avoiding data fragmentation that is detached from the geographical context.

[0048] The semantic tags include attraction type, service type, and tourist reviews, aiming to transform data from "geographic coordinates" into "tourism data with attribute meaning." These semantic tags add semantic description labels to each spatially anchored tourism entity. For example, "attraction type" could be historical sites, natural landscapes, or theme parks; "service type" could be "local specialty food" for a food street or "budget hotel / high-end guesthouse" for accommodation; and "tourist reviews" could be high-score tags for attractions or "clean and hygienic" and "convenient transportation" tags for accommodation.

[0049] The rule-based mapping relationships include intra-regional mapping relationships and inter-regional mapping relationships. Essentially, they establish logical data associations based on these "rule-based mapping relationships." Specifically, through "intra-regional mapping relationships" (such as the spatial distance and service connections between "highly rated historical sites" and surrounding "specialty food streets" and "mid-range accommodations" within a tourist area) and "inter-regional mapping relationships" (such as the transportation connections and complementary customer base between "old town cultural tourism areas" and "suburban natural tourism areas"), logical connections are constructed between data, allowing the model to reflect the intra-regional supporting facilities and inter-regional synergy of tourism resources.

[0050] After completing the above steps, this embodiment continues with step S2, which involves dividing the tourism data into several tourism regions and mapping them to administrative regions. The specific process is as follows: Figure 3 As shown, it includes the following steps: S21. Generate a tourism area segmentation threshold based on the attraction scores under the city.

[0051] The core of this step is to determine one or more "score thresholds" by quantifying attraction rating data and spatial distribution characteristics, which serve as the basis for dividing different tourist areas. The segmentation threshold is: the maximum score of attractions within a city, rounded down, minus 1.

[0052] The scenic spot scores mentioned in this embodiment need to be based on a unified rating dimension, such as standardizing the 1-5 star ratings of various platforms to 0-100 points.

[0053] S22, Generate scores for attractions within the city. A list of attractions with tourism area segmentation thresholds.

[0054] The list of attractions may include data such as spatial location (latitude and longitude), quality assessment (comprehensive score), functional classification (attraction type), administrative affiliation, and distinctive features, depending on the requirements.

[0055] S23. For each attraction in the attraction list, draw a circle with a radius of 10 to 50 km centered on the attraction's coordinates, forming a number of attraction coordinate circles.

[0056] S24. Determine whether the coordinate circles of each scenic spot intersect. If they intersect, merge the coordinate circles of that scenic spot; otherwise, leave them unchanged.

[0057] S25. Generate a number of scenic spot ranges and map the administrative region where each scenic spot range is located; if the administrative regions overlap, merge them into one region and designate that region as a tourist area; if they do not overlap, leave them unchanged and designate each scenic spot range as a tourist area.

[0058] By completing the above steps, relevant information and data for each tourist area can be obtained. After completing the above steps, continue to step S3.

[0059] S3. Map the tourism data within each tourism area and between different tourism areas to form a tourism map. Here, "mapping the tourism data within each tourism area and between different tourism areas" refers to using spatial computing and database association technologies to aggregate and transform data within and between tourism areas into a visualized and interactive tourism spatial data model.

[0060] The spatial computation described herein addresses the problem of "spatial data association," specifically by using GIS (Geographic Information System) spatial algorithms to aggregate data within a region and calculate relationships between regions. Within a region, data aggregation employs spatial inclusion algorithms to associate POI data (including latitude and longitude) such as attractions, food streets, and accommodations with tourist area boundaries (polygon vector data) to determine whether a POI belongs to a certain region, thus achieving spatial classification of data within the region (e.g., "Jinli Ancient Street belongs to the Chengdu urban tourist area"). The calculation of relationships between regions utilizes spatial distance algorithms (such as the Haversine formula) and network analysis algorithms to calculate the straight-line distance and accessibility between different tourist areas, and correlates this with visitor flow data to construct the spatial association logic between regions.

[0061] The database association technology mentioned above relies on spatial databases (such as PostGIS) to establish multi-level data association relationships, which includes two parts: basic association and association extension. The basic association refers to binding the "attribute information" (such as attraction ratings, accommodation prices, and food street opening hours) of POIs within the region with "spatial coordinates" to form a one-to-one correspondence between "spatial location and attribute description", such as "Dujiangyan Scenic Area (coordinates X, Y) - rating 95 points - opening hours 8:30-18:00".

[0062] The association extension binds the "traffic data" and "passenger flow data" between regions with the "region ID" to form an association table of "region A-region B-traffic time-passenger flow data", ensuring that the data can be called on demand.

[0063] S4. Users filter based on the travel map, and the travel map displays the corresponding travel data list according to the selection.

[0064] This step eliminates the need for users to manually sift through massive amounts of fragmented information. Users can quickly obtain a ranking of "high-value attractions" and structured information about supporting resources for their target area from the tourism data list displayed on the travel map. This tourism data list includes at least a ranking of attraction scores and a list of tourism resources. This information allows users to shorten decision-making time and improve the accuracy and efficiency of their travel planning.

[0065] As described above, the present invention can be well implemented.

Claims

1. A method for classifying and filtering urban tourism data, characterized in that, Includes the following steps: S1. Collect entities with coordinate attributes and tourism value in the city to form tourism data, and perform regional spatial semantic modeling on the tourism data. S2. Divide the tourism data into a number of tourism regions and establish a mapping relationship with administrative regions; S3. Map the tourism data within each tourism area and between different tourism areas to form a tourism map; S4. Users filter based on the travel map, and the travel map displays the corresponding travel data list according to the selection.

2. The method for classifying and filtering urban tourism data according to claim 1, characterized in that, The "entities with coordinate attributes and tourism value" mentioned in step S1 refer to tourism POI data with geospatial identifiers.

3. The method for classifying and filtering urban tourism data according to claim 2, characterized in that, The tourism POI data refers to point-like information data with specific tourism-related attributes and significance in geographic space, which includes at least basic geographic information, attribute description information, evaluation and rating information, and business information.

4. A method for classifying and filtering urban tourism data according to any one of claims 1 to 3, characterized in that, The "generating tourism data" mentioned in step S1 specifically includes the following steps: S11. Statistics on the distribution of tourist areas and attractions in the city; S12, Mark the highest-scoring attractions in each tourist area; S13, Mark the snack streets in each tourist area; S14. Mark the accommodation distribution in each tourist area; S15. Generate tourism data.

5. The method for classifying and filtering urban tourism data according to claim 4, characterized in that, The "regional spatial semantic modeling of the tourism data" mentioned in step S1 includes at least location association, semantic labels, and rule mapping relationships; the location association includes at least basic spatial anchoring and tourism area boundary association; the semantic labels include at least attraction type, service type, and tourist reviews; and the rule mapping relationships include intra-regional mapping relationships and inter-regional mapping relationships.

6. The method for classifying and filtering urban tourism data according to claim 5, characterized in that, Step S2, "dividing tourism data into a number of tourism areas and establishing a mapping relationship with administrative regions," specifically includes the following steps: S21. Generate a tourism area segmentation threshold based on the attraction scores under the city. S22, Generate scores for attractions within the city. A list of attractions with tourism area segmentation thresholds; S23. For each attraction in the attraction list, draw a circle with a radius of 10 to 50 km centered on the attraction's coordinates, forming a number of attraction coordinate circles; S24. Determine whether the coordinate circles of each scenic spot intersect. If they intersect, merge the coordinate circles of that scenic spot; if they do not intersect, leave them unchanged. S25. Generate a number of scenic spot ranges and map the administrative region where each scenic spot range is located; if the administrative regions overlap, merge them into one region and designate that region as a tourist area; if they do not overlap, leave them unchanged and designate each scenic spot range as a tourist area.

7. The method for classifying and filtering urban tourism data according to claim 4, characterized in that, The "mapping of tourism data within each tourism area and between tourism areas" mentioned in step S3 refers to using spatial computing and database association technologies to aggregate and transform data within and between tourism areas into a visualized and interactive tourism spatial data model.

8. The method for classifying and filtering urban tourism data according to claim 4, characterized in that, The tourism data list mentioned in step S4 includes at least a list of attraction score sequences and a list of tourism resources.

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