File travel data integrated management system based on user behavior analysis

Abstract

The invention discloses a text travel data integrated management system based on user behavior analysis, and relates to the technical field of data analysis management. The data integration management center is in communication connection with a data acquisition integration module, a user behavior analysis module, a user portrait construction module, a behavior trend evaluation module, a user grade division module, a resource allocation optimization module and a personalized recommendation module. According to the text travel data integrated management system based on user behavior analysis, through comprehensive collection and analysis of browsing, searching, purchasing and evaluation behavior data of the user, interest preference, consumption habits and travel requirements of the user are accurately described, personalized text travel products and service recommendation are provided for the user, and meanwhile, the user experience is improved. Recommended contents can be dynamically adjusted according to real-time feedback of the user, the latest requirements of the user can be met all the time, and therefore the satisfaction degree and loyalty of the user are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis management, and in particular to a cultural and tourism data integrated management system based on user behavior analysis. Background Art

[0002] With the improvement of people's living standards and the diversification of leisure methods, cultural tourism has become an important part of people's daily life. However, users' demand for cultural tourism products is becoming increasingly personalized and diversified, which puts higher requirements on the cultural tourism industry. In recent years, the rapid development of big data technology has provided strong technical support for cultural tourism data management. Through big data technology, it is possible to quickly process and analyze massive cultural tourism data and dig out valuable information hidden behind the data. In order to better meet user needs, the cultural tourism industry needs to use big data analysis technology to conduct in-depth analysis of user behavior, so as to accurately grasp market trends and user preferences.

[0003] In the existing technology, due to the diversity and uncertainty of user behavior, the same management strategy is used for users of different values, which will lead to unbalanced resource allocation. Therefore, how to divide the levels according to user behavior data to ensure the balance of resource allocation is the problem we need to solve. To this end, a cultural and tourism data integrated management system based on user behavior analysis is proposed. Summary of the invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a cultural tourism data integrated management system based on user behavior analysis, including a data integration management center, the data integration management center is communicatively connected with a data collection integration module, a user behavior analysis module, a user portrait construction module, a behavior trend evaluation module, a user level classification module, a resource allocation optimization module and a personalized recommendation module, wherein the modules are connected by electrical signals;

[0005] The data collection integration module is used to collect various behavioral data of users in cultural and tourism scenarios, such as browsing history, click behavior, dwell time, and consumption history. The data sources are extensive, including online platforms and offline scenarios, to achieve centralized data management and ensure the comprehensiveness and accuracy of the data;

[0006] The user behavior analysis module is used to pre-process the collected behavior data and conduct in-depth analysis of the user behavior data to identify the user's interest preferences, consumption habits, and behavior pattern characteristics, providing strong support for the construction and classification of user portraits;

[0007] The user portrait construction module constructs a user portrait based on the user behavior analysis results. The user portrait includes multiple dimensions such as the user's basic information, interest preferences, and consumption ability;

[0008] The behavior trend evaluation module comprehensively analyzes the user's behavior trends in cultural and tourism scenarios by combining user portraits and user behavior patterns, providing important basis for personalized recommendations and precision marketing.

[0009] The user level classification module divides users into different levels based on the key features and behavior trend evaluation results in the user portraits, ensuring that resources can be reasonably allocated according to the needs of users of different levels;

[0010] The resource allocation optimization module optimizes the management and allocation of cultural and tourism resources according to the user level classification results, ensures the balance and rationality of resource allocation, and improves the overall service quality and user satisfaction;

[0011] The personalized recommendation module provides users with personalized cultural tourism product and service recommendations based on user portraits and level classification results. The recommended content is in line with the user's interest preferences and consumption capacity, improves the accuracy and conversion rate of recommendations, enhances the user's sense of participation and experience, and promotes the long-term development of cultural tourism projects.

[0012] Preferably, in the data collection integration module, the process of collecting behavior data includes:

[0013] Use online platforms to deploy tracking codes (JavaScript tracking codes) and use API calls to collect various user behavior data in cultural and tourism scenarios;

[0014] Deploy sensors, cameras, and POS devices in offline scenarios to collect user behavior data. Online platforms include official websites, apps, and social media to record users’ browsing history, click behavior, and search keyword data. Offline scenarios include scenic spot entrances, ticket offices, and amusement facilities. By installing sensors and camera devices, users’ arrival time, stay time, and consumption record data are collected. During the collection process, the accuracy, completeness, and consistency of the data are ensured to avoid data duplication, missing, or errors.

[0015] Integrate the data collected from online platforms and offline scenarios to form a unified data view, including data format conversion, field mapping, data cleaning and data duplication processing steps. Use data conversion tools to convert data from different sources into a unified data format, and establish a field mapping table to map fields from different sources to a unified field name and data type;

[0016] Use ETL tools to import the integrated data into the data warehouse, ensure data integrity and consistency during the data import process, establish data tables, views and indexes in the data warehouse to facilitate subsequent data access and analysis, and use data governance tools for data management and metadata management, which will help to more comprehensively understand user behavior and optimize cultural and tourism products and services.

[0017] Preferably, in the user behavior analysis module, the process of deeply analyzing the user behavior data includes:

[0018] Perform pre-processing operations such as data cleaning and data conversion on the collected behavioral data;

[0019] Conduct in-depth analysis on the pre-processed user behavior data to identify the user's interest preferences, consumption habits and behavior pattern characteristics, and classify the user behavior data into different categories, namely browsing behavior, search behavior, and purchase behavior. Browsing behavior records the pages browsed by the user, the length of stay, and the number of views; search behavior records the user's search keywords and search result click information; and purchase behavior records the products purchased by the user, the quantity, the amount, and the time information;

[0020] Count the number of product purchases, spending amounts, consumption times, and number of consumers in a preset time period to analyze the overall trend of user behavior, and display the analysis results through visualization tools for intuitive understanding;

[0021] Describe and count each user's spending amount and spending frequency to understand the user's purchasing power and purchasing frequency, analyze the user's spending distribution, such as the spending amount distribution and the purchase quantity distribution, to identify the user's spending habits, calculate the user's cumulative spending amount ratio, and evaluate the user's contribution;

[0022] By analyzing the user's browsing, searching, and purchasing behavior sequences, we can identify the user's behavioral pattern characteristics and divide the users into different groups for refined operations.

[0023] Preferably, in the user portrait construction module, the process of constructing the user portrait includes:

[0024] Extract users’ basic information and behavior data, and define the dimensions of user portraits, namely basic information, interest preferences, and consumption capacity;

[0025] Extract user behavior data from online and offline channels, and extract key features such as purchase history, browsing habits, and search keywords from user behavior data;

[0026] Generate tags describing user attributes based on the extracted features, and combine the generated tags into a complete user profile to ensure that the profile can fully reflect the user's key characteristics and behavior patterns;

[0027] Verify the accuracy of the portrait by comparing it with known user data, adjust the strategy and algorithm for building the portrait based on the verification results, monitor changes in user behavior, and adjust the labels in the portrait in a timely manner.

[0028] Preferably, in the behavior trend evaluation module, the analysis process of the user's behavior trend in the cultural and tourism scene includes:

[0029] Integrate user portraits and extract key features such as purchase history, browsing habits, and search keywords from user behavior data to analyze user behavior patterns in different time periods and scenarios;

[0030] Match the user's current behavior pattern with the user portrait, identify the user's current interests and needs, and analyze the user's behavioral characteristics of preference types and activity participation in cultural and tourism scenarios;

[0031] Based on the key features of the extracted user behavior data, a user behavior trend prediction model is constructed using machine learning technology. Based on the user's historical behavior and current profile, the user's future behavior trend is predicted;

[0032] According to the results of the user behavior trend prediction model, calculate the user's behavior trend evaluation index and analyze the user's behavior direction and intensity in the cultural and tourism scene;

[0033] Combining user portraits and behavioral trend evaluation indexes, we comprehensively analyze users’ behavioral trends in cultural and tourism scenarios, identify the types of cultural and tourism activities, products or services that users are interested in, and divide users into different groups based on the results of behavioral trend analysis.

[0034] Preferably, the calculation expression of the behavior tendency evaluation index is:

[0035] ;

[0036] in, is the behavioral tendency assessment index, The number of cultural and tourism activities that users participate in. For the The weight of an activity reflects the importance of the activity to the user's behavior trend. For users in The actual number of behaviors in an activity, For the The number of benchmark behaviors for an activity reflects the average level of user participation. The variance of user behavior data is used for standardization to ensure that the number of behaviors between different activities is comparable. To adjust the index, it is used to control the influence of the variance item on the behavioral tendency evaluation index. The number of cultural and tourism keywords that users searched and followed, For the The weight of a keyword reflects the importance of the keyword to the user's interest. For users in Number of searches and attentions on keywords, The maximum number of single keyword searches and attention times among all users, used for standardization. is the adjustment coefficient used to control the influence of keyword searches and attention times on the behavior trend evaluation index. The value range is between 0 and 1.

[0037] Preferably, in the user level classification module, the process of classifying users includes:

[0038] Combine the prediction results of the user's future behavior trend with the key features in the user portrait to calculate the user's behavior trend evaluation index result;

[0039] Comprehensive user portraits and behavior trend evaluation indexes to analyze user behavior trends in cultural and tourism scenarios and identify the types of cultural and tourism activities, products or services that users are interested in;

[0040] According to the results of behavioral trend analysis, users are divided into different user levels, namely high-value users, active users, and potential users, and corresponding evaluation thresholds are matched for users of different levels;

[0041] Regularly evaluate user behavior trends and profile characteristics, and dynamically adjust user levels based on the evaluation results.

[0042] Preferably, a plurality of the user levels correspond to a plurality of the evaluation thresholds, wherein the evaluation thresholds include an upper threshold and a lower threshold;

[0043] The multiple user levels and the multiple evaluation thresholds satisfy the following relationship:

[0044] High value users ;

[0045] Active Users ;

[0046] Potential Users ;

[0047] in, is the behavioral tendency assessment index, is the lower threshold corresponding to high-value users and the upper threshold corresponding to active users, is the lower threshold corresponding to active users and the upper threshold corresponding to potential users, , .

[0048] Preferably, in the resource allocation optimization module, the process of optimizing the management and deployment of cultural and tourism resources includes:

[0049] According to the determined user level and historical behavior data, the resource requirements of users of different levels are predicted, and based on the resource demand prediction results, resource matching strategies for users of different levels are formulated to ensure the rationality and balance of resource allocation;

[0050] According to the matching strategy, resources are allocated to the scenarios required by users of different levels, and in the process of resource allocation, resource configuration is optimized to improve resource utilization efficiency;

[0051] Regularly update and maintain cultural and tourism resources to maintain the attractiveness and competitiveness of resources, provide personalized service experience based on the needs of users of different levels, regularly conduct data analysis and evaluation of resource allocation and service quality, identify problems and take measures to improve them.

[0052] Preferably, in the personalized recommendation module, the process of providing personalized cultural travel product and service recommendations includes:

[0053] Based on the key features and user behavior data in the user portrait, combined with the user level classification results, provide personalized recommendations for users of different levels;

[0054] Based on the user's actual behavior data, the user level to which they belong is evaluated. The evaluation results should be updated in real time to reflect the user's current status and level. Based on the user portrait and user level classification results, cultural and tourism products and services that meet the user's interests, preferences and spending power are screened, and the recommended content is diversified, including attractions, hotels, restaurants, and activities.

[0055] The selected recommendations are displayed to users in the form of lists or cards, including basic product information, pictures, prices, and user reviews, so that users can make decisions.

[0056] Through user surveys and satisfaction ratings, we collect user feedback on recommended content. Based on user feedback, we continuously optimize user portraits and grading standards, and analyze user needs and status.

[0057] The present invention provides a cultural and tourism data integrated management system based on user behavior analysis. It has the following beneficial effects:

[0058] 1. This cultural and tourism data integrated management system based on user behavior analysis can accurately depict users' interest preferences, consumption habits and travel needs by comprehensively collecting and analyzing users' browsing, searching, purchasing and evaluation behavior data, and then provide users with personalized cultural and tourism product and service recommendations. At the same time, it can dynamically adjust the recommended content according to users' real-time feedback to ensure that the users' latest needs are always met, thereby significantly improving user satisfaction and loyalty.

[0059] 2. This cultural and tourism data integrated management system based on user behavior analysis can predict the resource needs of users of different levels by deeply analyzing user behavior data, and provide strong support for cultural and tourism enterprises to formulate scientific resource matching strategies. According to the prediction results, cultural and tourism resources can be allocated and optimized in advance to avoid idle resources or excessive congestion. In addition, real-time monitoring of resource usage can timely discover and solve bottleneck problems in operations, and improve overall operational efficiency, which not only reduces the operating costs of the enterprise, but also improves resource utilization efficiency and customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a module structure diagram of a cultural and tourism data integrated management system based on user behavior analysis according to the present invention;

[0061] Figure 2 This is a flow chart for analyzing the behavior trends of users in the cultural and tourism scenarios of the present invention;

[0062] Figure 3 A flow chart for classifying users according to the present invention. DETAILED DESCRIPTION

[0063] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0064] The first embodiment, as Figure 1 , Figure 2 As shown, the present invention provides a technical solution: a cultural tourism data integrated management system based on user behavior analysis, including a data integration management center, the data integration management center is communicatively connected with a data acquisition integration module, a user behavior analysis module, a user portrait construction module, a behavior trend evaluation module, a user level classification module, a resource allocation optimization module and a personalized recommendation module, wherein the modules are connected by electrical signals;

[0065] The data collection integration module is used to collect various behavioral data of users in cultural and tourism scenarios, such as browsing history, click behavior, dwell time, and consumption records. The data sources are extensive, including online platforms and offline scenarios, to achieve centralized data management and ensure the comprehensiveness and accuracy of the data. The tracking code (JavaScript tracking code) is deployed on the online platform and the API call is used to collect various behavioral data of users in cultural and tourism scenarios. Sensors, cameras, and POS devices are deployed in offline scenarios to collect user behavior data. Online platforms include official websites, apps, and social media to record users' browsing history, click behavior, and search keyword data. Offline scenarios include scenic spot entrances, ticket offices, and amusement facilities. Sensors and camera equipment are installed to collect users' arrival time, dwell time, and consumption record data. During the process, ensure the accuracy, completeness and consistency of data, avoid data duplication, missing or errors, integrate the data collected from online platforms and offline scenarios, and form a unified data view, including data format conversion, field mapping, data cleaning and data duplication processing steps. Use data conversion tools to convert data from different sources into a unified data format, and establish a field mapping table to map fields from different sources to a unified field name and data type. Use ETL tools to import the integrated data into the data warehouse, ensure data integrity and consistency during the data import process, establish data tables, views and indexes in the data warehouse to facilitate subsequent data access and analysis, and use data governance tools for data management and metadata management, which will help to more fully understand user behavior and optimize cultural and tourism products and services;

[0066] The user behavior analysis module is used to pre-process the collected behavior data and conduct in-depth analysis of the user behavior data, identify the user's interest preferences, consumption habits, and behavior pattern characteristics, provide strong support for the construction and classification of user portraits, and perform data cleaning and data conversion pre-processing operations on the collected behavior data. Data cleaning removes noise and outliers, corrects or deletes erroneous and inconsistent data. Data conversion converts timestamps into specific dates and times, conducts in-depth analysis of the pre-processed user behavior data, identifies the user's interest preferences, consumption habits, and behavior pattern characteristics, and divides the user behavior data into different categories, namely browsing behavior, search behavior, and purchasing behavior. Among them, browsing behavior records the pages browsed by the user, the duration of stay, and the number of views. Search behavior Record the user's search keywords, search result click information, purchase behavior record the user's purchased products, quantity, amount, time information, according to the preset time period to count the product purchase quantity, consumption amount, consumption frequency and number of consumers, in order to analyze the overall trend of user behavior, and use visualization tools to display the analysis results for intuitive understanding, to conduct descriptive statistics on each user's consumption amount and consumption frequency, to understand the user's purchasing power and purchase frequency, to analyze the user's consumption distribution, such as the distribution of consumption amount and the distribution of purchase quantity, to identify the user's consumption habits, and calculate the user's cumulative consumption amount ratio, evaluate the user's contribution, by analyzing the user's browsing, searching, and purchasing behavior sequence, identify the user's behavior pattern characteristics, and divide the users into different groups for refined operations;

[0067] The user portrait construction module constructs user portraits based on the results of user behavior analysis. User portraits include multiple dimensions such as user basic information, interest preferences, and consumption capacity. It extracts user basic information data and behavior data, and defines the dimensions of user portraits, namely basic information, interest preferences, and consumption capacity. Among them, basic information includes age, gender, geographic location, occupation, etc. Interest preferences involve the topics, activity types, brand preferences, etc. that users are interested in in cultural and tourism activities. Consumption capacity includes user consumption level, purchase frequency, average transaction amount, etc. It extracts user behavior data from online and offline channels, and extracts key features such as purchase history, browsing habits, and search keywords from user behavior data. The purchase history extracts the key features of the product or service type, brand, and price range purchased by the user; the browsing habits analyze the pages browsed by the user, the dwell time, and the click-through rate, and identify the topics and types that the user is interested in; the search keywords analyze the user's search behavior, extract keywords and phrases, and understand the user's interests and needs. Based on the extracted features, generate tags that describe the user's attributes, and combine the generated tags into a complete user portrait to ensure that the portrait can fully reflect the user's key features and behavior patterns. By comparing with known user data, the accuracy of the portrait is verified. According to the verification results, the strategy and algorithm for building the portrait are adjusted, the changes in user behavior are monitored, and the tags in the portrait are adjusted in a timely manner;

[0068] The behavior trend evaluation module combines user portraits and user behavior patterns to comprehensively analyze the behavior trends of users in cultural and tourism scenarios, providing an important basis for personalized recommendations and precision marketing. It integrates user portraits and extracts key features of purchase history, browsing habits, and search keywords from user behavior data to analyze user behavior patterns in different time periods and scenarios, match the user's current behavior pattern with the user portrait, identify the user's current interests and needs, and analyze the user's behavior characteristics of preference types and activity participation in cultural and tourism scenarios. Based on the key features extracted from the user behavior data, a user behavior trend prediction model is constructed using machine learning technology. Based on the user's historical behavior and current portrait, the user's future behavior trend is predicted. According to the results of the user behavior trend prediction model, the user's behavior trend evaluation index is calculated, and the user's behavior direction and intensity in cultural and tourism scenarios are analyzed. Combined with the user portrait and the behavior trend evaluation index, the user's behavior trend in cultural and tourism scenarios is comprehensively analyzed to identify the cultural and tourism activities, products, or service types that the user is interested in, and users are divided into different groups according to the behavior trend analysis results.

[0069] Furthermore, the calculation expression of the behavior tendency evaluation index is:

[0070] ;

[0071] in, is the behavioral tendency assessment index, The number of cultural and tourism activities that users participate in. For the The weight of an activity reflects the importance of the activity to the user's behavior trend. For users in The actual number of behaviors in an activity, For the The number of benchmark behaviors for an activity reflects the average level of user participation. The variance of user behavior data is used for standardization to ensure that the number of behaviors between different activities is comparable. To adjust the index, it is used to control the influence of the variance item on the behavioral tendency evaluation index. The number of cultural and tourism keywords that users searched and followed, For the The weight of a keyword reflects the importance of the keyword to the user's interest. For users in Number of searches and attentions on keywords, The maximum number of single keyword searches and attention times among all users, used for standardization. is the adjustment coefficient used to control the influence of keyword searches and attention times on the behavior trend evaluation index. The value range is between 0 and 1. When the number or intensity of user behavior is much higher than the benchmark value, the first term (variance term) will tend to the negative exponential power of 0, thereby increasing the entire evaluation index. When the number of keywords searched or paid attention to by users increases, and the weight of these keywords is higher, the second term (exponential function term) will tend to 1, also increasing the entire evaluation index. and The distribution of will directly affect the contribution of different activities and keywords to the evaluation index, and adjust the index and adjustment factor Allows fine-tuning of formulas to suit different analysis needs and data characteristics;

[0072] The user level classification module divides users into different levels based on the key features and behavioral trend evaluation results in the user portrait, ensuring that resources can be reasonably allocated according to the needs of users of different levels;

[0073] Resource allocation optimization module, based on the user level classification results, optimizes the management and allocation of cultural and tourism resources, ensures the balance and rationality of resource allocation, and improves the overall service quality and user satisfaction;

[0074] The personalized recommendation module provides users with personalized cultural and tourism product and service recommendations based on user portraits and level classification results. The recommended content is in line with the user's interests, preferences and consumption capacity, improves the accuracy and conversion rate of recommendations, enhances the user's sense of participation and experience, and promotes the long-term development of cultural and tourism projects.

[0075] The second embodiment is based on the first embodiment. Figure 3 As shown, in the user level classification module, the process of classifying users includes:

[0076] Combine the prediction results of the user's future behavior trends and the key features in the user portrait to calculate the user's behavior trend evaluation index results. Combine the user portrait and behavior trend evaluation index to analyze the user's behavior trends in cultural and tourism scenarios, identify the types of cultural and tourism activities, products or services that the user is interested in, and divide the users into different user levels according to the behavior trend analysis results, namely high-value users, active users, and potential users. Match the corresponding evaluation thresholds for users of different levels, regularly evaluate the user's behavior trends and portrait features, and dynamically adjust the user's level according to the evaluation results;

[0077] Further, the multiple user levels correspond to multiple evaluation thresholds, wherein the evaluation threshold includes an upper threshold and a lower threshold;

[0078] Multiple user levels and multiple evaluation thresholds satisfy the following relationship:

[0079] High value users ; It means that the user is likely to continue high frequency, high consumption, and high interaction in the future. The user has frequently visited or used cultural tourism products or services in the past period of time (nearly 6 months), and each visit or use time is long. The user spends a high amount of money on cultural tourism products or services, and the consumption frequency is stable or on an upward trend. The user actively participates in interactive activities of cultural tourism products or services, such as comments, sharing, and likes, and the quality of interaction is high. The user continues to use the same cultural tourism product or service and has a high loyalty to the brand or platform;

[0080] Active Users ; indicates that the user may continue to visit or use cultural tourism products or services in the future, but the frequency and consumption amount may fluctuate. The user has regularly visited or used cultural tourism products or services in the past period of time (nearly 3 months), but the time of visit or use may be relatively short. The user has a certain amount of consumption on cultural tourism products or services, but the amount of consumption may not be as high as that of high-value users. The user occasionally participates in interactive activities of cultural tourism products or services, but the quality of interaction may vary depending on personal preferences. The user may have a certain interest in and use multiple cultural tourism products or services, but the loyalty may not be as high as that of high-value users.

[0081] Potential Users ; It means that the user is less likely to continue to visit or use cultural travel products or services in the future, but there is still potential for conversion. The user has occasionally visited or used cultural travel products or services in the past period of time (nearly 1 month), or has no visit or use records. The user has few or no consumption records on cultural travel products or services, but may have certain consumption potential. The user rarely or never participates in interactive activities of cultural travel products or services. The user may not understand or have little interest in cultural travel products or services, but may be converted into active users or high-value users in the future;

[0082] in, is the behavioral tendency assessment index, is the lower threshold corresponding to high-value users and the upper threshold corresponding to active users, is the lower threshold corresponding to active users and the upper threshold corresponding to potential users, , ;

[0083] In the resource allocation optimization module, the process of optimizing the management and deployment of cultural and tourism resources includes:

[0084] According to the determined user level and historical behavior data, predict the resource needs of users of different levels, and formulate resource matching strategies for users of different levels based on the resource demand prediction results to ensure the rationality and balance of resource allocation. According to the matching strategy, allocate resources to the scenarios required by users of different levels, and in the process of resource allocation, optimize resource allocation, improve resource utilization efficiency, regularly update and maintain cultural and tourism resources, maintain the attractiveness and competitiveness of resources, provide personalized service experience according to the needs of users of different levels, regularly analyze and evaluate data on resource allocation and service quality, identify problems and take measures to improve them;

[0085] In the personalized recommendation module, the process of providing personalized cultural tourism product and service recommendations includes:

[0086] Based on the key features and user behavior data in the user portrait, combined with the user level classification results, personalized recommendations are provided for users of different levels. According to the user's actual behavior data, the user level to which they belong is evaluated. The evaluation results should be updated in real time to reflect the user's current status and level. According to the user portrait and user level classification results, cultural and tourism products and services that meet the user's interests, preferences and consumption capabilities are screened. The recommended content is diversified, including attractions, hotels, restaurants, and activities. The screened recommended content is displayed to the user in the form of lists and cards. The displayed content includes basic product information, pictures, prices, and user reviews to facilitate users to make decisions. User feedback on recommended content is collected through user surveys and satisfaction ratings. Based on user feedback, the user portrait and level classification standards are continuously optimized to analyze user needs and status.

[0087] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A cultural and tourism data integration management system based on user behavior analysis, including a data integration management center, characterized in that: The data integration management center is communicatively connected with a data collection integration module, a user behavior analysis module, a user portrait construction module, a behavior trend evaluation module, a user level classification module, a resource allocation optimization module and a personalized recommendation module, wherein the electrical signals between the modules are connected; The data collection integration module is used to collect various behavioral data of users in cultural and tourism scenarios; The user behavior analysis module is used to pre-process the collected behavior data and conduct in-depth analysis of the user behavior data to identify the user's interest preferences, consumption habits, and behavior pattern characteristics; The user portrait construction module constructs a user portrait based on the user behavior analysis results. The user portrait includes multiple dimensions such as the user's basic information, interest preferences, and consumption ability; The behavior trend evaluation module comprehensively analyzes the user's behavior trend in the cultural and tourism scene by combining user portraits and user behavior patterns; The user level classification module classifies users into different levels based on the key features and behavior trend evaluation results in the user portraits; The resource allocation optimization module optimizes the management and deployment of cultural and tourism resources according to the user level classification results; The personalized recommendation module provides users with personalized cultural and tourism product and service recommendations based on user portraits and level classification results.

2. According to claim 1, a cultural and tourism data integrated management system based on user behavior analysis is characterized by: In the data collection integration module, the process of collecting behavior data includes: Use online platforms to deploy tracking codes and use API calls to collect various user behavior data in cultural and tourism scenarios; Deploy sensors, cameras, and POS devices in offline scenarios to collect user behavior data. Online platforms include official websites, apps, and social media to record users’ browsing history, click behavior, and search keyword data. Offline scenarios include scenic spot entrances, ticket offices, and amusement facilities. By installing sensors and camera devices, users’ arrival time, stay time, and consumption record data are collected. Integrate the data collected from online platforms and offline scenarios to form a unified data view. Use data conversion tools to convert data from different sources into a unified data format, and create a field mapping table to map fields from different sources to a unified field name and data type. Use ETL tools to import the integrated data into the data warehouse, create data tables, views, and indexes in the data warehouse, and use data governance tools to manage data and metadata.

3. According to claim 2, a cultural and tourism data integrated management system based on user behavior analysis is characterized by: In the user behavior analysis module, the process of in-depth analysis of user behavior data includes: Perform pre-processing operations such as data cleaning and data conversion on the collected behavioral data; Conduct in-depth analysis on the pre-processed user behavior data to identify the user's interest preferences, consumption habits and behavior pattern characteristics, and classify the user behavior data into different categories, namely browsing behavior, search behavior, and purchase behavior. Browsing behavior records the pages browsed by the user, the length of stay, and the number of views; search behavior records the user's search keywords and search result click information; and purchase behavior records the products purchased by the user, the quantity, the amount, and the time information; Count the number of product purchases, spending amounts, consumption times, and number of consumers in a preset time period to analyze the overall trend of user behavior and display the analysis results through visualization tools; Describe and count each user's spending amount and spending frequency to understand the user's purchasing power and purchasing frequency, analyze the user's spending distribution to identify the user's spending habits, and calculate the user's cumulative spending amount ratio to evaluate the user's contribution; By analyzing the user's browsing, searching, and purchasing behavior sequences, we can identify the user's behavior pattern characteristics and divide the users into different groups.

4. According to claim 3, a cultural and tourism data integrated management system based on user behavior analysis is characterized in that: In the user portrait construction module, the process of constructing the user portrait includes: Extract users’ basic information and behavior data, and define the dimensions of user portraits, namely basic information, interest preferences, and consumption capacity; Extract user behavior data from online and offline channels, and extract key features such as purchase history, browsing habits, and search keywords from user behavior data; Based on the extracted features, generate tags that describe user attributes, and combine the generated tags into a complete user profile; Verify the accuracy of the portrait by comparing it with known user data, adjust the portrait construction strategy and algorithm based on the verification results, monitor changes in user behavior, and adjust the labels in the portrait.

5. According to claim 4, a cultural and tourism data integrated management system based on user behavior analysis is characterized by: In the behavior trend evaluation module, the analysis process of the user's behavior trend in the cultural and tourism scene includes: Integrate user portraits and extract key features such as purchase history, browsing habits, and search keywords from user behavior data to analyze user behavior patterns in different time periods and scenarios; Match the user's current behavior pattern with the user portrait, identify the user's current interests and needs, and analyze the user's behavioral characteristics of preference types and activity participation in cultural and tourism scenarios; Based on the key features of the extracted user behavior data, a user behavior trend prediction model is constructed using machine learning technology. Based on the user's historical behavior and current profile, the user's future behavior trend is predicted; According to the results of the user behavior trend prediction model, calculate the user's behavior trend evaluation index and analyze the user's behavior direction and intensity in the cultural and tourism scene; Combining user portraits and behavioral trend evaluation indexes, we comprehensively analyze users’ behavioral trends in cultural and tourism scenarios, identify the types of cultural and tourism activities, products or services that users are interested in, and divide users into different groups based on the results of behavioral trend analysis.

6. According to claim 5, a cultural and tourism data integrated management system based on user behavior analysis is characterized by: The calculation expression of the behavior tendency evaluation index is: ； in, is the behavioral tendency assessment index, The number of cultural and tourism activities that users participate in. For the The weight of the activity, For users in The actual number of behaviors in an activity, For the The number of baseline behaviors for an activity, is the variance of user behavior data, To adjust the index, The number of cultural and tourism keywords that users searched and followed, For the The weight of the keywords, For users in Number of searches and attentions on keywords, The maximum number of searches and attentions for a single keyword among all users. is the adjustment coefficient used to control the influence of keyword searches and attention times on the behavior trend evaluation index. The value range is between 0 and 1.

7. The cultural and tourism data integrated management system based on user behavior analysis according to claim 6 is characterized by: In the user level classification module, the process of classifying users includes: Combine the prediction results of the user's future behavior trend with the key features in the user portrait to calculate the user's behavior trend evaluation index result; Comprehensive user portraits and behavior trend evaluation indexes to analyze user behavior trends in cultural and tourism scenarios and identify the types of cultural and tourism activities, products or services that users are interested in; According to the results of behavioral trend analysis, users are divided into different user levels, namely high-value users, active users, and potential users, and corresponding evaluation thresholds are matched for users of different levels; Regularly evaluate user behavior trends and profile characteristics, and dynamically adjust user levels based on the evaluation results.

8. The cultural and tourism data integrated management system based on user behavior analysis according to claim 7 is characterized by: A plurality of the user levels correspond to a plurality of the evaluation thresholds, wherein the evaluation thresholds include an upper threshold and a lower threshold; The multiple user levels and the multiple evaluation thresholds satisfy the following relationship: High value users ; Active Users ; Potential Users ; in, is the behavioral tendency assessment index, is the lower threshold corresponding to high-value users and the upper threshold corresponding to active users, is the lower threshold corresponding to active users and the upper threshold corresponding to potential users, , .

9. A cultural and tourism data integrated management system based on user behavior analysis according to claim 8, characterized in that: In the resource allocation optimization module, the process of optimizing the management and deployment of cultural and tourism resources includes: According to the determined user level and historical behavior data, predict the resource needs of users of different levels, and formulate resource matching strategies for users of different levels based on the resource demand prediction results; According to the matching strategy, resources are allocated to the scenarios required by users of different levels, and resource allocation is optimized during the resource allocation process; Regularly update and maintain cultural and tourism resources, provide personalized service experience based on the needs of users of different levels, and regularly conduct data analysis and evaluation of resource allocation and service quality.

10. The cultural and tourism data integrated management system based on user behavior analysis according to claim 9, characterized in that: In the personalized recommendation module, the process of providing personalized cultural travel product and service recommendations includes: Based on the key features and user behavior data in the user portrait, combined with the user level classification results, provide personalized recommendations for users of different levels; Based on the user's actual behavior data, the user level is evaluated, and based on the user portrait and user level classification results, cultural and tourism products and services that meet the user's interest preferences and consumption capacity are screened; The selected recommended content is displayed to the user in the form of a list or card, including the basic information, pictures, prices, and user reviews of the product; Through user surveys and satisfaction ratings, we collect user feedback on recommended content. Based on user feedback, we continuously optimize user portraits and grading standards, and analyze user needs and status.

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