Hierarchical Data Display Method and System Based on E-commerce Platform

Abstract

The present invention relates to the technical field of data display, and particularly to a hierarchical data display method and system based on an e-commerce platform. The method includes the following steps: obtaining structured data of the e-commerce platform; constructing an initial display interface of the e-commerce platform without login for the structured data of the e-commerce platform to generate an initial e-commerce platform display interface; obtaining user login data; analyzing the range of staying goods for the initial e-commerce platform display interface based on the user login data to obtain staying goods data; predicting the types of goods preferred by users for the staying goods data to generate predicted data on the types of goods preferred by users. The present invention improves the accuracy and real-time performance of hierarchical data display by constructing a hierarchical e-commerce display interface, performing eye movement tracking and attention perception on users, and being compatible with the display of different device screens.

Description

Technical Field

[0001] The present invention relates to the technical field of data display, and particularly to a hierarchical data display method and system based on an e-commerce platform. Background Art

[0002] In the early days when e-commerce platforms just emerged, web design mainly relied on simple HTML and CSS, and the presentation of product information was relatively straightforward. Users needed to continuously scroll the page to find information, resulting in low efficiency. With the growth of demand, e-commerce platforms began to introduce classification and filtering functions. The technological development at this stage included the optimization of the backend database to support the management and rapid retrieval of products in different categories. On the front end, filtering conditions were added, enabling users to quickly narrow down the search scope according to attributes such as price, brand, and size. With the development of big data and artificial intelligence, e-commerce platforms utilized user behavior data for data mining and analysis to achieve personalized recommendations. This required powerful algorithms that could recommend products in line with users' preferences based on their historical browsing and purchase behaviors, further enhancing the shopping experience and sales efficiency. However, currently, traditional e-commerce platforms do not explicitly consider the distribution of users' attention, which may lead to some important information being overlooked, and there may be problems with the display effects on different devices, such as poor display effects on small-screen devices, resulting in low accuracy and real-time performance of hierarchical data display. Summary of the Invention

[0003] Based on this, it is necessary to provide a hierarchical data display method and system based on an e-commerce platform to solve at least one of the above technical problems.

[0004] To achieve the above object, a hierarchical data display method based on an e-commerce platform, the method includes the following steps:

[0005] Step S1: Obtain the structured data of the e-commerce platform; construct an initial display interface of the e-commerce platform without login for the structured data of the e-commerce platform to generate an initial e-commerce platform display interface;

[0006] Step S2: Obtain user login data; analyze the range of products stayed on the initial e-commerce platform display interface based on the user login data to obtain the stayed product data; predict the types of products preferred by users for the stayed product data to generate predicted data on the types of products preferred by users; adjust the sorting of recommended products on the initial e-commerce platform display interface through the predicted data on the types of products preferred by users to generate a logged-in e-commerce platform display interface;

[0007] Step S3: Analyze the user's attention area on the logged-in e-commerce platform display interface based on the device camera to generate the user's attention distribution area; adjust the ambient perception brightness of the logged-in e-commerce platform display interface according to the user's attention distribution area to generate an adjusted interface of the logged-in e-commerce platform;

[0008] Step S4: Perform device screen area compatibility adjustment on the adjusted interface of the logged-in e-commerce platform to generate a device screen display interface; conduct an A / B test on the device screen display interface and the adjusted interface of the logged-in e-commerce platform to generate display interface test data; perform device screen adaptation on the adjusted interface of the logged-in e-commerce platform through the display interface test data, so as to generate an e-commerce platform hierarchical display interface.

[0009] By obtaining the structured data of the e-commerce platform, the present invention can help understand information such as the types, sales volumes, and prices of products on the platform, providing a data basis for subsequent personalized recommendations and displays. The analysis of the range of products stayed by users based on user login data and the prediction of the types of favorite products can predict the types of user preferences according to the user's past browsing and purchasing behaviors, thereby optimizing the display interface and providing recommended products that better meet the user's interests. The analysis of the user's attention area and the adjustment of the ambient perception brightness adjust the display interface according to the user's attention distribution on the page and the ambient brightness, making it easier for the user to notice the products of interest, improving the user experience and purchase rate. The device screen area compatibility and adaptation ensure good display on different devices (computers, tablets, mobile phones), improving the responsive design of the website. Through A / B testing, according to user feedback and data analysis, the display interface is adjusted and optimized to further improve the user experience and the conversion rate of the page. The entire process can help optimize aspects such as the layout, typesetting, and color matching of the interface, making the page more attractive and user-friendly, reducing user fatigue, increasing the user's stay time and browsing depth. The ambient perception brightness adjustment can automatically adjust the page brightness according to the light conditions of the user's environment, improving the visibility of the page and enabling the user to comfortably browse products in different environments. Based on data analysis and user behavior prediction, the optimization of the display interface is more targeted and effective, and the sorting and display of recommended products can be adjusted in real time according to the user's product stay data and attention distribution data, continuously optimizing the presentation method of the page. Through personalized recommendations, interface optimization, and responsive design, the user's needs can be better met, increasing the possibility and satisfaction of user purchases. The improvement of the compatibility and usability of the interface will also reduce the possibility of user loss due to interface problems, increasing user retention and loyalty. Therefore, the present invention improves the accuracy and real-time performance of hierarchical data display by constructing a hierarchical e-commerce display interface, performing eye movement tracking and attention perception on users, and simultaneously achieving display compatibility for different device screens.

[0010] In this specification, a hierarchical data display system based on an e-commerce platform is provided for executing the above-mentioned hierarchical data display method based on an e-commerce platform. The hierarchical data display system based on an e-commerce platform includes:

[0011] An initial interface construction module, which is used to obtain structured data of an e-commerce platform; construct an initial display interface of the e-commerce platform without login for the structured data of the e-commerce platform, and generate an initial display interface of the e-commerce platform.

[0012] A preference analysis module, which is used to obtain user login data; perform an analysis on the range of staying products for the initial display interface of the e-commerce platform based on the user login data to obtain staying product data; predict the types of products preferred by users for the staying product data to generate predicted data on the types of products preferred by users; adjust the sorting of recommended products for the initial display interface of the e-commerce platform through the predicted data on the types of products preferred by users to generate a logged-in display interface of the e-commerce platform.

[0013] An eye movement tracking module, which is used to analyze the user's attention area for the logged-in display interface of the e-commerce platform based on the device camera to generate the user's attention distribution area; adjust the ambient perception brightness for the logged-in display interface of the e-commerce platform according to the user's attention distribution area to generate an adjusted interface of the logged-in e-commerce platform.

[0014] A device compatibility module, which is used to make the adjusted interface of the logged-in e-commerce platform compatible with the device screen area to generate a device screen display interface; perform an A / B test on the device screen display interface and the adjusted interface of the logged-in e-commerce platform to generate display interface test data; perform device screen adaptation on the adjusted interface of the logged-in e-commerce platform through the display interface test data, thereby generating a hierarchical display interface of the e-commerce platform.

[0015] The beneficial effects of the present invention are as follows: By generating a logged-in display interface of the e-commerce platform according to user login data and predicted types of preferred products, it can present personalized product recommendations according to the interests and preferences of users, improving the user shopping experience and satisfaction. Analyzing the user's attention area based on the device camera and adjusting the brightness according to ambient perception can make the display interface more in line with the user's visual habits, enhancing the comfort and shopping experience of users on the platform. Making the adjusted interface of the logged-in e-commerce platform compatible with the device screen area and performing an A / B test ensure that the interfaces presented on different devices can adapt to various screen sizes and resolutions, thereby improving the accessibility and coverage of the platform. Through data analysis and A / B test in steps S2 and S4, it can timely discover changes in the user's behavior patterns and preferences, and thus make corresponding adjustments and optimizations to continuously improve the display effect and user satisfaction of the e-commerce platform. The personalized display and optimized interface can more accurately meet the user's needs, increase the click-through rate and purchase intention of users for the recommended products, and further improve the conversion rate and sales volume of the e-commerce platform. Therefore, the present invention improves the accuracy and real-time performance of hierarchical data display by constructing a hierarchical e-commerce display interface, performing eye movement tracking and attention perception on users, and making display compatibility for different device screens. Description of the Drawings

[0016] Figure 1 It is a schematic diagram of the step flow of a hierarchical data display method based on an e-commerce platform;

[0017] Figure 2 It is Figure 1 a schematic diagram of the detailed implementation steps of step S2 in

[0018] Figure 3 It is Figure 1 a schematic diagram of the detailed implementation steps of step S3 in

[0019] Figure 4 It is Figure 1 a schematic diagram of the detailed implementation steps of step S4 in

[0020] The realization, functional characteristics and advantages of the purpose of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific Embodiments

[0021] The technical method of the present invention for a patent will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0023] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0024] To achieve the above object, please refer to Figures 1 to 4 , the present invention provides a hierarchical data display method based on an e-commerce platform, including the following steps:

[0025] Step S1: Obtain the structured data of the e-commerce platform; construct the initial display interface of the e-commerce platform without login for the structured data of the e-commerce platform to generate the initial e-commerce platform display interface;

[0026] Step S2: Obtain the user login data; conduct an analysis of the range of products stayed on the initial e-commerce platform display interface based on the user login data to obtain the product stay data; predict the types of products preferred by the user for the product stay data to generate the predicted data of the types of products preferred by the user; adjust the sorting of recommended products for the initial e-commerce platform display interface through the predicted data of the types of products preferred by the user to generate the logged-in e-commerce platform display interface;

[0027] Step S3: Conduct an analysis of the user's attention area on the logged-in e-commerce platform display interface based on the device camera to generate the user attention distribution area; adjust the ambient perception brightness of the logged-in e-commerce platform display interface according to the user attention distribution area to generate the adjusted interface of the logged-in e-commerce platform;

[0028] Step S4: Make the logged-in e-commerce platform adjusted interface compatible with the device screen area to generate the device screen display interface; conduct an A / B test on the device screen display interface and the logged-in e-commerce platform adjusted interface to generate the display interface test data; adapt the logged-in e-commerce platform adjusted interface to the device screen through the display interface test data, thereby generating the hierarchical display interface of the e-commerce platform.

[0029] The present invention can help understand information such as the types, sales volumes, and prices of products on the e-commerce platform by obtaining structured data of the e-commerce platform, providing a data basis for subsequent personalized recommendations and displays. The analysis of the range of products stayed on based on user login data and the prediction of the types of products preferred can predict the types of preferences of users according to the users' past browsing and purchasing behaviors, so as to optimize the display interface and provide recommended products that better meet the users' interests. The analysis of the user's attention area and the adjustment of the ambient perception brightness adjust the display interface according to the attention distribution of the user on the page and the ambient brightness, making it easier for the user to notice the products of interest and improving the user experience and purchase rate. The compatibility and adaptability of the device screen area ensure good display on different devices (computers, tablets, mobile phones), improving the responsive design of the website. Through A / B testing, according to user feedback and data analysis, the display interface is adjusted and optimized to further improve the user experience and the conversion rate of the page. The entire process can help optimize aspects such as the layout, typesetting, and color matching of the interface, making the page more attractive and user-friendly, reducing user fatigue, and increasing the user's stay time and browsing depth. The adjustment of the ambient perception brightness can automatically adjust the page brightness according to the light conditions of the user's environment, improving the visibility of the page and enabling the user to browse products comfortably in different environments. Based on data analysis and user behavior prediction, the optimization of the display interface is more targeted and effective. The sorting and display of recommended products can be adjusted in real time according to the data of the products stayed on by the user and the attention distribution data, continuously optimizing the presentation method of the page. Through personalized recommendations, interface optimization, and responsive design, the user's needs can be better met, increasing the possibility and satisfaction of the user's purchase. The improvement of the compatibility and usability of the interface will also reduce the possibility of user loss due to interface problems, increasing user retention and loyalty. Therefore, the present invention improves the accuracy and real-time performance of hierarchical data display by constructing a hierarchical e-commerce display interface, performing eye movement tracking and attention perception on users, and ensuring display compatibility for different device screens.

[0030] In an embodiment of the present invention, with reference to Figure 1 as described, it is a schematic diagram of the step flow of a method for hierarchical data display based on an e-commerce platform according to the present invention. In this example, the method for hierarchical data display based on an e-commerce platform includes the following steps:

[0031] Step S1: Obtain structured data of the e-commerce platform; construct an initial display interface of the e-commerce platform without login for the structured data of the e-commerce platform to generate an initial e-commerce platform display interface;

[0032] In the embodiments of the present invention, by obtaining the structured data of the e-commerce platform, it can be achieved by calling the API and the database. The data may include product information, price, sales volume, evaluation, etc. Clean and preprocess the obtained data, remove duplicate items, missing values, and incomplete data, and perform data format conversion and standardization to ensure the consistency and usability of the data. Analyze and screen the cleaned data, and select suitable data fields and content for display according to requirements. This may involve sorting out and extracting the classification, labels, attributes, etc. of the products. Based on the screened data, use front-end development technologies (such as HTML, CSS, JavaScript, etc.) to build an initial display interface of the non-login e-commerce platform. This includes designing the page layout, style, and interaction effects, and dynamically presenting the data on the page. Ensure that the page has a good display effect on different devices, including the PC side, mobile side, etc. Adopt responsive design technology to enable the page to adaptively adjust according to the screen size and resolution of the device, providing a good user experience. Optimize the performance of the page, including reducing the page loading time, optimizing the loading of images and resources, compressing the code, etc., to improve the loading speed and user experience of the page.

[0033] Step S2: Obtain user login data; perform an analysis on the range of products stayed on the initial display interface of the e-commerce platform based on the user login data to obtain product stay data; predict the types of products preferred by users for the product stay data to generate predicted data on the types of products preferred by users; adjust the sorting of recommended products on the initial display interface of the e-commerce platform through the predicted data on the types of products preferred by users to generate a display interface of the logged-in e-commerce platform;

[0034] In the embodiments of the present invention, obtain user login data through the user login system of the e-commerce platform or other authentication mechanisms, including the user's login time, stay time, browsing records, etc. Analyze the user login data to identify the range of products where the user stays on the initial display interface of the e-commerce platform. This can be achieved by counting the length of time the user stays on the page and the types of products accessed. Process and organize the obtained product stay data, which may include removing duplicate items, counting the number of stays for each product, etc. Based on the product stay data, use machine learning or other prediction models to predict the types of products preferred by users. This can be achieved through classification algorithms or clustering algorithms to identify the user's preferences. According to the prediction model, predict the types of products preferred by users and generate corresponding data for subsequent adjustment of the sorting of recommended products. According to the predicted data on the types of products preferred by users, adjust the sorting of products on the initial display interface of the e-commerce platform, and recommend products that are more in line with the user's preferences to the user. This can be achieved by re-sorting the product list or increasing the display of specific types of products. Generate a display interface of the logged-in e-commerce platform according to the adjusted sorting of recommended products for the user to browse and purchase after logging in.

[0035] Step S3: Analyze the user's attention area on the display interface of the logged-in e-commerce platform based on the device camera to generate the user's attention distribution area; adjust the ambient perception brightness of the display interface of the logged-in e-commerce platform according to the user's attention distribution area to generate the adjusted interface of the logged-in e-commerce platform;

[0036] In the embodiment of the present invention, real-time image data of the user on the display interface of the logged-in e-commerce platform is obtained by using the device camera. The obtained image data is analyzed by using computer vision technology to identify the user's attention area. This can be achieved through technologies such as object detection, face recognition, or key point detection. According to the analyzed user's attention area, data of the user's attention distribution area is generated. This can be a heat map or other forms of data representation. Based on the data of the user's attention distribution area, combined with the ambient perception technology, the brightness of the display interface of the logged-in e-commerce platform is adjusted. This can adjust the brightness of each area according to the position and intensity of the user's attention area to improve the user experience and visual comfort. According to the adjusted brightness data, the adjusted interface of the logged-in e-commerce platform is generated. This interface will adjust the brightness according to the user's attention distribution area, enabling the user to browse the e-commerce platform more comfortably.

[0037] Step S4: Make the adjusted interface of the logged-in e-commerce platform compatible with the device screen area to generate the device screen display interface; conduct an A / B test on the device screen display interface and the adjusted interface of the logged-in e-commerce platform to generate the display interface test data; adapt the adjusted interface of the logged-in e-commerce platform to the device screen through the display interface test data, thereby generating the hierarchical display interface of the e-commerce platform.

[0038] In the embodiment of the present invention, the adjusted interface of the logged-in e-commerce platform is adjusted according to the screen size and resolution of different devices to make it compatible with the screen areas of various devices. This can be achieved through responsive design or adaptive layout to ensure good display on different devices. According to the adjusted interface that is compatible with each device, a display interface adapted to different device screens is generated. This interface will be adjusted according to the screen size and resolution of the device to ensure that the content can be fully displayed on different devices. Conduct an A / B test on the device screen display interface and the original adjusted interface of the logged-in e-commerce platform. This can compare the performance of the two interfaces in terms of user experience and other metrics to determine which interface is more suitable for users. According to the A / B test results, display interface test data is generated. These data include the user's click-through rate, dwell time, conversion rate, etc., which are used to evaluate the effects of the two interfaces. According to the display interface test data, the adjusted interface of the logged-in e-commerce platform is adapted to the device screen. This includes adjusting the layout, font size, picture size, etc. to improve the display effect and user experience on different devices.

[0039] Preferably, step S1 includes the following steps:

[0040] Step S11: Obtain structured data of the e-commerce platform, where the structured data of the e-commerce platform includes e-commerce texts and e-commerce images;

[0041] Step S12: Perform data preprocessing on the e-commerce texts to generate standard e-commerce text data, where the data preprocessing includes data cleaning, filling of missing data values, and data standardization; perform image preprocessing on the e-commerce images to generate standard e-commerce images, where the image preprocessing includes image brightness enhancement, image geometric transformation, and image standardization;

[0042] Step S13: Based on the standard e-commerce text data and the standard e-commerce images, construct an initial display interface of the non-login e-commerce platform to generate an initial e-commerce platform display interface, where the initial e-commerce platform display interface includes a home page display interface, a search display interface, a product display interface, and a product settlement interface.

[0043] In the present invention, by obtaining and preprocessing the structured data of the e-commerce platform, including text and image data, it helps to improve the quality and accuracy of the data, reduces the noise and errors in the data, and makes subsequent analysis and applications more reliable. The generated initial display interface of the non-login e-commerce platform, including a home page display interface, a search display interface, a product display interface, and a product settlement interface, helps to improve the user experience. Users can browse products, conduct searches, select products, and complete settlements more easily, enhancing the satisfaction and shopping experience of users on the e-commerce platform. Through the preprocessing and analysis of e-commerce text and image data, the preferences and behaviors of users can be better understood. This helps the e-commerce platform to achieve more accurate personalized recommendations, display products more in line with the interests and needs of users, and improve the sales conversion rate and user satisfaction. The standardized e-commerce text data and image data, as well as the constructed initial e-commerce platform display interface, can provide more effective tools and platforms for the operation and management of the e-commerce platform. Operators can manage product information and optimize page displays more conveniently, improving the operation efficiency and management level of the platform. Through the preprocessing and analysis of the structured data, more data support and decision-making references can be provided for the e-commerce platform. Operators can conduct more in-depth data analysis based on the standardized data, discover information such as user behavior patterns and market trends, and thus formulate more effective operation strategies and decisions.

[0044] In the embodiments of the present invention, structured data including text and image information is crawled from an e-commerce platform. The structured data provided by the e-commerce platform is obtained by using methods such as API interfaces or database queries. Noise data in the text, such as HTML tags, special characters, etc., is removed. For missing data, filling can be performed using methods such as mean, median, or other statistical methods to fill in the missing values. The text data is standardized, such as converting to a unified encoding format, unifying case, etc. The brightness of the image is adjusted to improve the clarity and contrast of the image. Geometric transformations such as scaling, rotating, and cropping are performed on the image to adapt to different display requirements of sizes and shapes. The image is converted to a unified format, such as JPEG, PNG, etc., and compressed to reduce storage space and loading time. A home page that attracts users' attention is designed to display information such as popular products and promotional activities to guide users to browse and purchase. A search function is provided, and relevant products are displayed according to the keywords entered by the user to facilitate the user to find the target product. Product information is displayed in the form of a list or grid, including product name, price, evaluation, etc., and a link to the product details page is provided. A shopping cart function is provided to display the products selected by the user and the settlement process, including filling in the delivery information, selecting the payment method, etc., and finally completing the order.

[0045] Preferably, the construction process of the home page display interface in step S13 includes the following steps:

[0046] Semantic association is performed on the standard e-commerce text data and the standard e-commerce image to obtain e-commerce product information data;

[0047] Based on the e-commerce product information data, e-commerce product information sales volume analysis is performed to generate e-commerce product information sales volume data; according to the e-commerce product information sales volume data, priority sorting of the e-commerce product information data for home page display is performed to generate an initial e-commerce home page display interface;

[0048] Analysis of the same product brand style is performed on the e-commerce product information data to generate same product brand style data; according to the same product brand style data, the first-order adjustment of the product sorting on the initial e-commerce home page display interface is performed to generate e-commerce home page product first-order adjustment data;

[0049] Based on the e-commerce home page product first-order adjustment data, product discount analysis is performed on the e-commerce product information data to generate product discount analysis data; through the product discount analysis data, the second-order adjustment of the product sorting on the e-commerce home page product first-order adjustment data is performed to generate e-commerce home page second-order adjustment data;

[0050] The initial e-commerce home page display interface is dynamically adjusted to a non-login e-commerce home page display interface by using the e-commerce home page second-order adjustment data, thereby generating the home page display interface.

[0051] Through semantic association of standard e-commerce text data and images, the present invention can more accurately match product information, thereby realizing personalized homepage display and meeting the personalized needs of users. Based on the sales volume data of e-commerce product information, the priority ranking of homepage display is carried out, so that hot-selling products can be preferentially displayed on the homepage, improving user click-through rate and purchase conversion rate, and increasing sales revenue. By analyzing the same brand style of products, the homepage display can be made more unified and orderly, enhancing the user's shopping experience, brand recognition and loyalty. By analyzing product discounts, products with large discount efforts can be highlighted in the homepage display, attracting users' attention and promoting sales growth. Using the e-commerce homepage second-order adjustment data to dynamically adjust the initial homepage display interface, the display interface can be optimized according to real-time data and user behavior, improving user satisfaction and shopping experience.

[0052] In the embodiment of the present invention, semantic analysis of e-commerce text data is carried out by using natural language processing technology to understand information such as product descriptions and attributes. Computer vision technology is used to analyze e-commerce image data to extract information such as product features, colors, and shapes. The text data and image data are associated to establish semantic associations between products and their descriptions and images. Based on historical sales data or real-time sales data, sales volume analysis of products is carried out to understand the sales situation of each product. Data mining or machine learning technology can be used to discover information such as sales trends and hot-selling products. According to the product sales volume data, the priority ranking of homepage display of products is carried out, and products with high sales volume are ranked in the front for display to improve the click-through rate and conversion rate. By analyzing features such as the brand and style of products, brand recognition and style analysis can be carried out by using natural language processing and image processing technologies. According to the same brand style data, the product sorting of the initial homepage display interface is adjusted so that products of the same brand or the same style are displayed together, enhancing the user experience. The discount information of products is analyzed, including discount strength, discount duration, etc., to attract users' attention and promote purchases. Combining user behavior data and real-time sales data, the homepage display interface is dynamically adjusted to recommend products that meet the user's interests and sales popularity.

[0053] Preferably, the construction process of the search display interface in step S13 includes the following steps:

[0054] Design a search box for e-commerce product information data to obtain a product search box; extract product attribute features from e-commerce product information data to obtain e-commerce product attribute feature data, where the e-commerce product attribute feature data includes product name, product category, and product brand;

[0055] Design a filter for the product search box based on the product name, product category, and product brand to obtain a product search filter; perform a first-round search on the e-commerce product information data according to the product search filter to obtain the first-round product search display data; perform a second-round search on the first-round product search display data according to the product search box to obtain the second-round product search display data;

[0056] Perform product association connection on the first-round product search display data and the second-round product search display data to generate product association path data; design product cards for the e-commerce product information data according to the product path data to generate associated product cards;

[0057] Perform dynamic loading of product search on the associated product cards based on the partial refresh technology to generate product search dynamic loading cards; use the product search filter and the product search box to sort the search results of the product search dynamic loading cards to generate e-commerce product search sorting data;

[0058] Classify the e-commerce product search sorting data into high-priority product search data and low-priority product search data; use the high-priority product search data and the low-priority product search data to adjust the search association path of the product association path data, so that the product association path data corresponding to the high-priority product search data is greater than the product association path data corresponding to the low-priority product search data, thereby generating a search display interface.

[0059] Through the design of the product search box and filter in the present invention, users can quickly find the required products, improving the search efficiency and satisfaction of users. According to the product attribute feature data and search history, the system can provide personalized product recommendations for users, increasing users' recognition of the relevance of search results. Through multi-round searches and product association connections, the search results are more in line with users' needs, improving the accuracy and integrity of search results. The use of the partial refresh technology to dynamically load search results improves the page loading speed, enabling users to obtain search results faster and enhancing the user experience. According to the settings of the search box and filter, sort the search results, making it easier for users to find the required products and improving users' satisfaction with search results. Divide the search results into high-priority and low-priority, which helps to prominently display the products that users are most interested in, improving the attractiveness and click-through rate of search results. By preferentially displaying the product association paths corresponding to high-priority data, it strengthens users' attention to relevant products and improves users' willingness to click and purchase search results.

[0060] In an embodiment of the present invention, by designing an input box with a search function, users can enter keywords for searching. Attribute feature data such as product names, product categories, and product brands are extracted from e-commerce product information data. According to attribute features such as product names, product categories, and product brands, a filter is designed for users to further filter in the search results. According to the settings of the filter, the first-round search is performed on the e-commerce product information data to obtain the first-round product search display data. According to the keywords entered by the user in the search box, the second-round search is performed on the first-round product search display data to obtain the second-round product search display data. The first-round and second-round product search display data are associated and connected to generate product association path data. According to the product path data, product cards are designed to display relevant product information. Using the partial refresh technology, the associated product cards are dynamically loaded to improve the page loading speed and user experience. According to the conditions in the filter and the search box, the search results of the product search dynamic loading cards are sorted to generate e-commerce product search sorting data. The search sorting data is classified by priority to obtain high-priority product search data and low-priority product search data. According to the search priority data, the product association path data is adjusted so that the product association path data corresponding to the high-priority data is greater than the low-priority data, thereby generating the search display interface.

[0061] Preferably, the construction process of the product display interface in step S13 includes the following steps:

[0062] Based on the search display interface, the layout of e-commerce product cards is performed on the e-commerce product information data to generate e-commerce product card layout data, where the e-commerce product card layout data includes product pictures, product price data, product title data, and product rating data;

[0063] The product pictures are classified by product type to generate product type data; based on the product type data, a three-dimensional model is constructed to generate a three-dimensional model of the product type; according to the three-dimensional model of the product type, the scenario application simulation of the e-commerce product is performed to generate the application scenario of the e-commerce product;

[0064] The e-commerce product card layout data is imported into the e-commerce product application scenario for product attribute visualization to generate an e-commerce product virtual interaction scenario; according to the e-commerce product virtual interaction scenario, product information is displayed to generate e-commerce product virtual display information data;

[0065] According to the e-commerce product virtual display information, a comment community is constructed for the product price data, product title data, and product rating data to generate e-commerce product comment community data; the e-commerce product comment community data and the e-commerce product virtual display information data are modularly integrated to generate the product display interface.

[0066] Through the layout of product cards, the construction of 3D models, and the simulation of scenario applications, users can obtain a more intuitive and realistic experience when browsing products, thus enhancing their shopping experience. Through the virtual display and visualization of product information, the features and advantages of products can be presented more vividly, attracting users' attention and thereby increasing the sales conversion rate. By building a comment community, users can communicate and share during the shopping process, increasing interaction among users and enhancing user stickiness. Through modular integration, product information, the comment community, and virtual display information can be organically combined to improve the transmission efficiency and integrity of product information. Through the display of virtual interaction scenarios, the brand image and reputation of the e-commerce platform can be enhanced, and users' trust in the platform can be strengthened.

[0067] In the embodiments of the present invention, by collecting and organizing e-commerce product information data, including information such as product pictures, prices, titles, and ratings. Classify the product pictures, which can be done using image recognition technology or manual annotation to divide the product pictures into different types, preparing for subsequent 3D model construction. Based on the product type data, use 3D modeling software such as Blender, 3ds Max, etc. to construct the corresponding 3D models of product types. The specific 3D model construction process first requires obtaining product type data, which may include information such as the size, shape, and material of the product. Use professional 3D modeling software (such as Blender, Maya, etc.) to model according to the product type data. Designers can create the corresponding 3D models based on the description and reference images of the product type. Optimize the model to ensure that the details and proportions of the model conform to the actual product. Add details such as textures and lighting to make the model more realistic, thus obtaining the corresponding 3D models of product types. During the model construction process, consider the details and realism of the model to ensure a realistic effect can be presented during the scene application simulation stage. Use virtual reality technology or scene simulation software to apply the 3D models of product types to the scene for simulation to display the usage scenarios of the product in different environments. The specific scene simulation process is to build a scene in the 3D modeling software, which can be the virtual store environment of an e-commerce platform. Include elements such as floors, walls, and lights. Place the previously created 3D models of product types in appropriate positions in the scene to simulate the placement of products in an actual store. By adjusting the camera perspective and lighting, simulate the display effects of the product in different environments, such as simulating day, night, and lighting effects. During the simulation process, different lighting, background, and other factors can be considered to improve the realism and attractiveness of the simulation. Import the e-commerce product card layout data into the virtual interaction scene for product attribute visualization to ensure that the product information is clearly visible. The specific product attribute visualization process is that the product card layout data may include information such as the name, price, and rating of the product, and import these data into the scene. Design the UI style of the product card, such as the size, color, and font of the card. Associate the product attributes with the corresponding 3D models. For example, when the user clicks on a certain product card, the corresponding 3D model will be selected or highlighted. Consider the interaction method between the user and the scene and design a user experience-friendly virtual interaction scene, including aspects such as perspective control and information display. The specific virtual interaction scene construction process includes designing the interaction method of the user in the virtual scene, such as clicking on the product card to view detailed information, dragging the product to the shopping cart, etc. Use interaction design tools or code to implement these interaction functions in the scene. For example, when the user clicks on the product card, a window with the detailed information of the product pops up. Ensure that the user can freely browse products, view details, and perform operations similar to actual shopping in the virtual scene.Based on the virtual display information of e-commerce products, build a review community platform for users to evaluate, communicate, and share products. Consider the functional design of the review community, including functions such as review posting, liking, and replying, to improve user participation and interactivity. Integrate the review community data and virtual display information data modularly to ensure data integrity and consistency. The specific modular integration process includes integrating the virtual display information data of products (such as product attributes, 3D model associations, etc.) and the product review community data, and designing different functional modules (such as product display, review area, shopping cart, etc.) into independent modules. Integrate these modules according to the designed layout and logic to form the final product display interface. Ensure that each module can interact and communicate with each other. Design a suitable interface layout and interaction method to display the integrated data on the product display interface, providing users with a comprehensive product information display and interactive experience.

[0068] Preferably, the construction process of the product settlement interface in step S13 includes the following steps:

[0069] Based on the product display interface, confirm the product information display to generate product information display confirmation data; select the product delivery address according to the product information display confirmation data to generate product delivery address filling data; select the product transportation method based on the product delivery address filling data to generate product transportation method selection data;

[0070] Confirm the address information of the buyer and seller for the product transportation method selection data to obtain the buyer location information data and the seller location information data; estimate the delivery time through the buyer location information data and the seller location information data to generate the estimated product delivery time;

[0071] Confirm the product payment method for the product information display confirmation data according to the estimated product delivery time to obtain the product payment method data; encrypt the product payment method data for payment security to generate product payment encryption data; confirm the product order based on the product payment encryption data to generate product settlement data; visualize the product settlement data to obtain the product settlement interface.

[0072] Through steps such as confirming product information, selecting a delivery address, and choosing a shipping method, users can complete the shopping process more conveniently, enhancing their shopping experience and satisfaction. When users confirm product information and fill in the delivery address, they can avoid order errors or delivery problems caused by inaccurate information, improving the accuracy and reliability of orders. By estimating the expected delivery time, users can better arrange their time and also learn in advance about the arrival time of the product, enhancing the timeliness of delivery and user convenience. Securely encrypting payment method data can effectively protect users' payment information from being stolen or leaked, enhancing users' trust and security in the payment process. The product order confirmation stage can ensure the accuracy of information such as the products selected by the user, the delivery address, and the shipping method, reducing the likelihood of order errors and improving the efficiency and accuracy of order processing. After visualizing the product settlement data, it can be presented to users in an intuitive manner, enabling users to more clearly understand the content of the order and settlement details, enhancing users' management and control capabilities over the order.

[0073] In an embodiment of the present invention, when the user browses products, the system displays information such as product pictures, names, and prices, and provides a confirmation button or option for the user to confirm whether to purchase. After confirmation, the system generates product information display confirmation data. After the user confirms the purchase, the system provides an interface for selecting a delivery address. The user can select from the existing address list or manually enter a new address. After selection, the system generates product delivery address filling data. After selecting the delivery address, the system displays available shipping methods, such as express delivery, logistics, etc. After the user selects, the system generates corresponding product shipping method selection data. Before confirming the order, the system displays the address information of both the buyer and the seller to ensure the accuracy of the user and seller information. Based on the user and seller address information, the system calculates the expected delivery time, considering factors such as the shipping method and distance. This information can be displayed to the user on the settlement interface. When the user confirms the order, the user selects a payment method, such as credit card, Alipay, etc. The system generates product payment method data based on the user's selection. The user's payment information needs to undergo secure encryption processing to ensure user data security. The system uses an encryption algorithm to encrypt the payment information to ensure the security of the payment process. After the user confirms the order, the system generates product settlement data, including product information, delivery address, shipping method, payment method, etc. After the user confirms and submits the order. The system presents the generated product settlement data to the user in an intuitive manner, such as on an order overview page or a settlement confirmation page. The user can clearly view the order details and settlement information.

[0074] Preferably, step S2 includes the following steps:

[0075] Step S21: Obtain user login data;

[0076] Step S22: Obtain user key - touch position information based on user login data to get user key - touch position information data; analyze the user key - touch stay time for the user key - touch position information data to generate user key - touch stay time data;

[0077] Step S23: Analyze the range of displayed items during the stay for the initial e - commerce platform display interface according to the user key - touch stay time data to obtain stay item data; divide the stay item data into data sets to generate a model training set and a model test set;

[0078] Step S24: Use the convolutional neural network algorithm to train the model training set to generate a user - preferred item training model; test the user - preferred training model according to the model test set to generate a user - preferred item prediction model; import the stay item data into the user - preferred item prediction model to predict the type of user - preferred items and generate user - preferred item type prediction data;

[0079] Step S25: Adjust the sorting of recommended items for the initial e - commerce platform display interface through the user - preferred item type prediction data to generate a logged - in e - commerce platform display interface.

[0080] Through analyzing the stay time and behaviors of users on the e - commerce platform, combined with model training and prediction, the present invention can generate a user - preferred item prediction model, thereby realizing personalized item recommendation. This can improve the user experience, increase users' interest in and willingness to purchase the recommended items. By showing users the items they may be interested in, it can improve their decision - making speed and confidence in purchasing, thus increasing the conversion rate. Because the recommended items are more in line with users' preferences and are more likely to arouse the desire to purchase. According to the user - preferred item type prediction data, adjust the item sorting on the display interface of the e - commerce platform, making it easier for users to find the items they are interested in. This can improve users' satisfaction and loyalty to the e - commerce platform. Through personalized recommendation and optimized interface, it can increase the stay time of users on the e - commerce platform and enhance user stickiness. Users are more inclined to shop on a platform that can meet their needs, thus increasing the possibility of revisiting. Through the analysis of user behavior data and model prediction, data - driven decision - making can be achieved, continuously optimizing the operation strategy and user experience of the e - commerce platform, and improving business benefits and competitiveness.

[0081] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0082] Step S21: Obtain user login data;

[0083] In the embodiments of the present invention, login data is obtained through the account system record during user login. This method can capture key data such as the user's login time and login device information. In the backend system of an e-commerce platform, a logging function can be set up to record user login events. These logs can include the user's IP address, user agent information, login time, etc. By analyzing these log data, the user's login behavior information can be obtained. Use a third-party authentication service, such as an OAuth service provider or an authentication service provided by a social media platform, to obtain the user's login data. These services usually provide APIs to obtain the user's login information. After the user logs in, the login status of the user can be tracked by setting cookies or sessions. In this way, when the user accesses other pages, it can be confirmed whether the user has logged in by reading the cookie or session, and relevant information such as the login time is recorded. By embedding monitoring tools, such as Google Analytics, Mixpanel, etc., in the front-end page, the user's behavior, including the login behavior, can be monitored in real time. These tools can record data such as the user's login time and page browsing situation.

[0084] Step S22: Obtain user key-touch position information based on the user login data to obtain user key-touch position information data; analyze the user key-touch stay time for the user key-touch position information data to generate user key-touch stay time data;

[0085] In the embodiments of the present invention, JavaScript code is embedded in the front-end page of the e-commerce platform to capture the user's mouse click position information. The browser-provided event listener can be used to capture the user's click event and obtain the coordinates of the click position. The captured user click position information is sent to the backend server for recording. The data can be sent to the backend through an Ajax request or other means. The user click position information is stored in a database. The database table can include fields such as user ID, timestamp, page URL, click position coordinates, etc. Preprocess the collected user key-touch position information data, including data cleaning, deduplication, formatting, etc., for subsequent analysis. According to the user key-touch position information data, calculate the stay time of the user on each page. The stay time can be estimated by calculating the time interval between consecutive clicks on the same page by the user. Use data analysis tools (such as Pandas, NumPy in Python) to perform statistical analysis on the stay time data. The average stay time of each page, the distribution of the stay time, etc. can be calculated. Visualize the analysis results, such as drawing a histogram of the stay time, generating a heat map of the stay time, etc., to more intuitively understand the user behavior.

[0086] Step S23: Analyze the range of products stayed on the initial e-commerce platform display interface based on the user's key-touch staying time data to obtain the stayed product data; divide the stayed product data into data sets to generate a model training set and a model test set;

[0087] In the embodiments of the present invention, by using the user's key-touch staying time data, the range of products stayed by the user on the e-commerce platform is identified. The products with a relatively long staying time on the page by the user can be identified. The product data related to the user's staying time is screened out from the overall product data, including information such as product ID, category, sales volume, price, etc. The extracted stayed product data is sorted and cleaned to ensure data quality and consistency. The stayed product data is divided into a model training set and a model test set. The commonly used division ratio is that 80% of the data is used for training and 20% of the data is used for testing. When dividing the data set, the randomness of the data should be ensured to avoid sample selection bias. For supervised learning tasks, labels need to be assigned to each data sample, such as whether the user has purchased the product or the user's evaluation of the product, etc. Ensure the balanced distribution of product data of various categories in the training set and the test set to improve the generalization ability of the model.

[0088] Step S24: Use the convolutional neural network algorithm to train the model training set to generate a user-preferred product training model; test the user-preferred training model according to the model test set to generate a user-preferred product prediction model; import the stayed product data into the user-preferred product prediction model to predict the type of user-preferred products and generate user-preferred product type prediction data;

[0089] In the embodiments of the present invention, by using the model training set generated in step S23, which includes the data of the parked goods and the corresponding labels (whether the user purchases or other preference labels), a convolutional neural network model is constructed. The specific construction process is to design the structure of the convolutional neural network, including convolutional layers, pooling layers, fully connected layers, etc. The data of the parked goods is used as the input data, and after being processed and feature extracted, it is input into the convolutional neural network. According to the task requirements, the output layer is designed. Since it is a multi-class classification problem, it is necessary to classify and predict different types of goods. The convolutional neural network is trained using the model training set to optimize the network parameters so that it can better fit the features of the parked goods data. During the training process, the backpropagation algorithm and optimizers (such as Adam, SGD, etc.) are used to update the model parameters. The prediction accuracy of the model depends on factors such as the quality of the training data of the model, the network structure design, and the hyperparameter adjustment. By using more and more representative parked goods data for training, the prediction accuracy of the model can be improved, which is used to learn the user's preference pattern. CNN is suitable for processing image data and can effectively extract the features of goods. The CNN model is trained using the training set, and the model parameters are continuously adjusted through the backpropagation algorithm so that the model can accurately predict the types of goods preferred by the user. The model test set generated in step S23 is used, which also includes the data of the parked goods and the corresponding labels. The trained CNN model is used to predict the data in the model test set to obtain the prediction results of the goods preferred by the user. By comparing the differences between the model prediction results and the actual labels, the performance of the model can be evaluated, including indicators such as accuracy and recall rate. The parked goods data obtained in step S23 is imported into the trained prediction model for the goods preferred by the user. The trained model is used to predict the parked goods data to obtain the prediction results of the types of goods preferred by the user. The prediction results are associated with the corresponding goods data to generate the prediction data for the types of goods preferred by the user.

[0090] Step S25: Adjust the sorting of the recommended goods in the initial e-commerce platform display interface through the prediction data of the types of goods preferred by the user to generate a personalized e-commerce platform display interface.

[0091] In the embodiments of the present invention, by using the predicted data of the user's preferred commodity types as input and combining appropriate recommendation algorithms, such as collaborative filtering, content filtering, deep learning models, etc., the commodities on the e-commerce platform are sorted and adjusted. The recommendation algorithm can perform recommendation sorting based on factors such as the user's historical behavior, preference tags, and the behavior of similar users. According to the predicted data of the user's preferred commodity types, the commodity types related to the user's preferences are preferentially displayed in the display interface. Considering that the user may be interested in multiple commodity types, multiple prediction results can be comprehensively considered for sorting adjustment. A personalized logged-in e-commerce platform display interface is generated, and the display order of commodities is adjusted according to the predicted data of the user's preferred commodity types, making it easier for the user to find the commodities they are interested in. Considering that the user's preferences may change over time, the recommendation algorithm can be updated regularly and the commodity sorting can be readjusted. According to the result of the recommended commodity sorting adjustment, the display interface of the e-commerce platform is designed and interactively optimized to ensure that the user can conveniently browse and purchase the recommended commodities. Considering the display effects and user habits of different devices, corresponding responsive design and user experience optimization are carried out. A feedback mechanism is set up to collect the feedback opinions of the user on the personalized display interface and the purchase behavior data. According to the user feedback and purchase situation, the recommendation algorithm and the display interface are evaluated and adjusted to continuously optimize the personalized recommendation effect.

[0092] Preferably, step S3 includes the following steps:

[0093] Step S31: Based on the device camera, collect the user's eye image of the logged-in e-commerce platform display interface to obtain the user's eye image; perform heat map analysis on the user's eye image to generate the user's eye behavior data;

[0094] Step S32: Perform user eye movement tracking on the logged-in e-commerce platform display interface according to the user's eye behavior data to generate the user's eye movement tracking data, where the user's eye movement tracking data includes the user's page fixation points, the user's saccade path data, and the user's dwell time data;

[0095] Step S33: Analyze the user's attention area on the logged-in e-commerce platform display interface through the user's page fixation points, the user's saccade path data, and the user's dwell time data to generate the user's attention distribution area; use the user's attention distribution area to perform commodity waterfall flow adjustment on the logged-in e-commerce platform display interface to generate a commodity waterfall flow adjustment display mode;

[0096] Step S34: Perform eye color system brightness detection on the user's eye image according to the commodity waterfall flow adjustment display mode to generate an eye environment perception brightness value; adjust the interface brightness of the logged-in e-commerce platform display interface through the eye environment perception brightness value to generate an adjusted interface of the logged-in e-commerce platform.

[0097] The present invention collects the user's eye images through the device camera, and then performs heat map analysis to obtain the attention distribution of the user on the display interface and the eye movement data. These data help to understand the user's attention level and browsing patterns for different regions. Eye movement tracking is carried out using the eye movement data, including page fixation points, saccade paths, and dwell time data, etc. These data can help analyze the user's attention distribution and browsing habits on the display interface, so as to generate the user attention distribution area. Through the user attention distribution area, the commodity waterfall flow adjustment is carried out. This means that on the display interface, according to the user's attention level, the display method and position of the commodity are adjusted, so that the user can more easily notice the commodities of interest. According to the commodity waterfall flow adjustment display mode, the eye color system brightness detection is carried out on the user's eye image to generate the eye environment perception brightness value. This step can adjust the brightness of the display interface according to the brightness of the environment where the user is located to improve the user's visual experience. According to the user's eye movement and attention distribution, the personalized adjustment of the display interface is realized to enhance the user experience. Through heat map analysis and eye movement tracking, the user behavior is better understood, and the user is targeted to focus on the key points. The interface brightness is adjusted according to the eye environment perception brightness value, so that the user can enjoy a comfortable visual experience in different environments. Improving the user's stay time and attention on the display interface helps to improve the purchase conversion rate and increase the sales opportunity.

[0098] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0099] Step S31: Based on the device camera, collect the user's eye images on the display interface of the landing e-commerce platform to obtain the user's eye images; perform heat map analysis on the user's eye images to generate the user's eye movement data;

[0100] In the embodiments of the present invention, by setting a device camera on the display interface of the e-commerce platform, it is ensured that the eye images of the user can be captured. Among them, for the logged-in scenario, user authorization for camera image acquisition is required. If the user does not grant authorization, it will jump to the non-logged-in e-commerce platform display interface. The device camera obtains the eye images of the user when browsing the e-commerce platform through real-time shooting and recording. This may require the use of computer vision technology for real-time processing and analysis. The collected eye images are preprocessed, including removing noise, adjusting the image size and brightness, etc., to improve the accuracy and efficiency of subsequent analysis. Using heatmap analysis technology, the eye images are processed to determine the fixation points and attention areas of the user on the display interface. This can be achieved by detecting the position and movement trajectory of the eyes in the image. The specific heatmap analysis process is to use an eye tracking device (such as an eye tracker) to capture the eye movements of the user, perform real-time processing on the eye images obtained by the eye tracking device, extract the position and movement trajectory of the eyes, and generate a heatmap based on the position data of the eyes to display the attention areas of the user on the page. According to the results of the heatmap analysis, user eye behavior data is generated, including information such as the position of the fixation points, the fixation duration, and the saccade path. These data will be used for subsequent user eye tracking and attention area analysis. The generated eye behavior data is stored in a database and further analyzed and processed. This may involve using machine learning algorithms or statistical methods to identify the behavior patterns and trends of the user.

[0101] Step S32: Perform user eye tracking on the logged-in e-commerce platform display interface according to the user eye behavior data to generate user eye tracking data, where the user eye tracking data includes the user's page fixation points, user saccade path data, and user stay time data;

[0102] In the embodiment of the present invention, by preparing user eye behavior data, including information such as the user's fixation points and eye movement trajectories, these data can be obtained through eye image acquisition and heat map analysis in step S31. Using computer vision technology, process the user eye behavior data, identify and extract the user's fixation points on the page, that is, the areas where the user stays for a relatively long time during browsing. Analyze the user eye behavior data to determine the user's saccade path on the page, that is, the movement trajectory of the user's eyes on the page. This can be achieved by detecting the order and continuity between fixation points, where the user saccade path data is converted into specific fixation point position coordinates by converting the attention degree of each pixel point on the heat map. According to the user's fixation point positions on the page, these position points are combined into a saccade path in chronological order, and the saccade path data is presented in the form of a line chart or a trajectory chart to visually display the user's saccade process on the page. Calculate the time the user stays in each page area according to the distribution and duration of the fixation points. This can help understand the user's degree of attention and interest points in different areas. Integrate the user eye tracking data into the database and conduct further analysis. This may include the analysis of user behavior patterns, preferences and trends, as well as the correlation analysis with other metrics (such as click-through rate, conversion rate, etc.).

[0103] Step S33: Analyze the user attention areas of the logged-in e-commerce platform display interface through the user page fixation points, user saccade path data, and user stay time data to generate the user attention distribution area; use the user attention distribution area to perform a product waterfall flow adjustment on the logged-in e-commerce platform display interface to generate a product waterfall flow adjusted display mode;

[0104] In the embodiments of the present invention, data such as the user's page fixation points, saccade paths, and dwell times are collected. These data may be collected through an eye-tracking device or software. The collected data is preprocessed, including data cleaning, denoising, and format conversion, for subsequent analysis. The collected user eye movement data is used for attention area analysis. Visualization tools such as heatmaps can be used to display the attention distribution of the user on the page. Based on data such as the user's fixation points, saccade paths, and dwell times, the areas with higher user attention, that is, the attention areas, are determined. More specifically, for user attention area analysis, the user fixation point data is converted into a heatmap to show the attention distribution of the user on the page. This can be achieved using professional tools or programming libraries, such as the JavaScript library D3.js or the Python library matplotlib. The highlighted areas on the heatmap represent the hotspots on the page that the user is looking at, that is, the parts that the user pays more attention to. The user saccade path data is converted into a visualization path map to show the movement trajectory of the user on the page. This helps to understand the user's browsing pattern and order. The user dwell time data is converted into a visualization chart or bar chart to show the average dwell time in different areas. The areas with long dwell times are the key areas that the user is interested in. According to the distribution of the user attention areas, the layout of the e-commerce platform display interface and the product display method are adjusted, and the waterfall flow display mode is adopted. Products or content related to the user's interests are preferentially displayed within the attention areas to increase the user's attention and click-through rate for these areas. Considering the size, arrangement, and display position of the products, the overall page layout is made more in line with the user's browsing habits and attention distribution characteristics. According to the adjusted product display layout, a waterfall flow adjustment display mode for products is generated. This includes determining the display order, arrangement method, and page layout structure of the products. The effectiveness and user experience of the new display mode can be verified by simulating user browsing behavior or conducting user surveys, etc.

[0105] Step S34: Perform eyeball color system brightness detection on the user's eyeball image according to the waterfall flow adjustment display mode for products, and generate an eyeball environment perception brightness value; adjust the interface brightness of the logged-in e-commerce platform display interface through the eyeball environment perception brightness value to generate an adjusted interface for the logged-in e-commerce platform.

[0106] In the embodiments of the present invention, an eye image of a user is obtained by using an eye tracking device or a camera. The obtained eye image is processed to extract the eye region. Image processing techniques, such as color space conversion, filtering, and threshold segmentation, are used to detect the color and brightness of the eye region. By analyzing the brightness value of the eye region, the brightness level of the environment around the eye can be quantified according to different algorithms and models, which helps to understand the ambient brightness where the user is located. Specifically, for the brightness detection of the eye color system, for each pixel in the key region, the brightness (such as the gray value) of the surrounding pixels is calculated. The brightness values of the surrounding pixels of each pixel are statistically analyzed, and a certain window size can be used for statistics, such as taking the average value. Combining the position of the user's eye and the pixel brightness of the key region, the perceived brightness value of the eye environment is calculated. According to the position of the eye and the pixel brightness distribution of the key region, the brightness situation of the environment where the user is currently located is analyzed. According to the perceived brightness value of the eye environment, the interface brightness of the logged-in e-commerce platform display interface is adjusted. By adjusting the background color, text color, picture brightness, etc. of the page, the overall brightness of the page is made to match the brightness level of the environment around the eye. For a darker environment, the brightness and contrast of the page can be increased to improve the visibility and recognition of the page content; for a brighter environment, the brightness of the page can be reduced to avoid glare and discomfort caused by the page being too bright. According to the adjusted interface brightness, an adjusted logged-in e-commerce platform display interface is generated. Ensure that the adjusted interface can provide a good user experience in different lighting environments, while maintaining the clarity and readability of the page content. Among them, according to the perceived brightness value of the user's eye environment and design requirements, a brightness adjustment strategy is defined. For example: if the ambient light is darker, the overall brightness of the page is increased to improve readability. If the ambient light is brighter, the overall brightness of the page is reduced to avoid eye fatigue. According to the defined brightness adjustment strategy, the brightness of each element in the platform display interface is adjusted accordingly. For text: adjust the color and transparency of the text according to the background brightness to make it more clearly readable. For the background: adjust the brightness and contrast of the background color or picture to make the overall page look more comfortable. Consider user interface preferences. For example, some users may prefer a darker theme, while others may prefer a brighter theme. Different theme options can be provided according to user preferences, or users can be allowed to customize the brightness settings.

[0107] Preferably, step S4 includes the following steps:

[0108] Step S41: Analyze the device screen of the adjusted interface of the logged-in e-commerce platform to generate the device screen display area; cut the adjusted interface of the logged-in e-commerce platform according to the device screen display area to generate the device screen display interface;

[0109] Step S42: Conduct an A / B test on the device screen display interface and the adjusted interface of the logged-in e-commerce platform to generate display interface test data; monitor user behavior on the adjusted interface of the logged-in e-commerce platform through the display interface test data to generate user usage feedback data;

[0110] Step S43: Perform device screen adaptation on the device screen display interface according to the user usage feedback data, thereby generating a hierarchical display interface for the e-commerce platform.

[0111] Through the analysis and interface cutting of the device screen, and the device screen adaptation according to the user feedback data, the present invention can ensure that the interface of the e-commerce platform can be well displayed on different devices, improving the cross-device user experience. By conducting an A / B test, the performance of different versions of the interface in terms of user behavior and feedback can be compared, so as to find out more effective interface design and layout solutions, optimizing the user experience and page effect. By monitoring user behavior and collecting user feedback data, the preferences and behavior habits of users for the interface can be deeply understood, providing a strong basis for interface design and function improvement. After the device screen adaptation is performed according to the user usage feedback data, the generated hierarchical display interface of the e-commerce platform can better adapt to different devices and user needs, improving the user experience and page usability.

[0112] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0113] Step S41: Analyze the device screen of the adjusted interface of the logged-in e-commerce platform to generate the device screen display area; perform interface cutting on the adjusted interface of the logged-in e-commerce platform according to the device screen display area to generate a device screen display interface;

[0114] In the embodiments of the present invention, the screens of different devices are analyzed by using appropriate tools or technologies, such as browser developer tools, screen resolution detection tools, etc. These tools can help determine the screen resolution and display area of various devices. The device screen analysis specifically includes obtaining the screen size of the user device, usually in inches. Obtaining the screen resolution of the device, usually in pixels, such as 1920x1080. Calculating the pixel density of the device, that is, the number of pixels per inch. Calculating the screen display area of the device according to the screen resolution and pixel density. Determining the layout of the page according to the calculated screen display area of the device. Adjusting the arrangement and size of page elements according to different device screen sizes and display areas so that they can be properly displayed on different devices. Using responsive design techniques, such as CSS media queries, Flexbox, etc., to adjust the page layout and element sizes according to the device screen size. Calculating the display area of each device according to the analyzed device screen resolution. This can be obtained through simple mathematical calculations, such as multiplying the screen width by the height. Determining how to cut the interface of the landing e-commerce platform according to the display area of each device to adapt to the screen sizes of different devices. This may involve adjusting the layout, changing the element sizes or positions, etc. This step needs to consider maintaining the consistency and usability of the interface. Generating display interfaces for different device screen sizes according to the results of interface cutting. This may require UI design or front-end development work to ensure that the interface can be correctly displayed and presented on various devices. After generating the device screen display interface, conduct tests to ensure that the performance of the interface on various devices meets the expectations. Make necessary adjustments and optimizations according to the test results to improve the user experience and the usability of the interface.

[0115] Step S42: Conduct an A / B test on the device screen display interface and the adjusted interface of the landing e-commerce platform to generate display interface test data; monitor the user behavior of the adjusted interface of the landing e-commerce platform through the display interface test data to generate user usage feedback data;

[0116] In the embodiments of the present invention, the device screen display interface and the adjusted interface of the logged-in e-commerce platform are respectively marked as version A and version B. Use an A / B testing tool or platform, such as Google Optimize, Optimizely, etc., to set up an experimental group and a control group to ensure that two groups of users can be randomly assigned to different interface versions. During the operation of the A / B test, collect test data on the two versions of the interface. These data can include indicators such as page views, clicks, conversion rates, etc. Use statistical analysis tools, such as Google Analytics, etc., to analyze and compare the collected data to evaluate the performance differences between the two versions of the interface. During the A / B test, monitor the behavior of users on the two versions of the interface. This can be achieved through user behavior analysis tools, logging, etc. The specific steps of the A / B test include clarifying the objectives to be tested, such as increasing the user click-through rate, increasing user retention rate, etc., and selecting the indicators used to measure the objectives, such as the number of page clicks, user stay time, conversion rate, etc. Test group (Group A): device screen display interface; control group (Group B): adjusted interface of the logged-in e-commerce platform. Randomly assign users who visit the website or application to the test group and the control group. Ensure the randomness of the grouping to eliminate potential biases. Within a set period of time, let the users in the test group see the device screen display interface (Group A), and let the users in the control group see the adjusted interface of the logged-in e-commerce platform (Group B). Record the user behavior data of each group, including indicators such as the number of clicks, stay time, conversion rate, etc. Collect user behavior data such as clicks, browsing, and stay time, and conduct analysis to understand the interaction between users and the interface. Combine the display interface test data and the user behavior monitoring data to generate user usage feedback data. According to the behavior and feedback of users, evaluate indicators such as the usability and user satisfaction of the interface, and identify potential problems or areas for improvement. The specific user behavior monitoring is to record the click behavior of users on the page, including the click location, click element, click time, etc., record the scrolling behavior of users on the page, including the scrolling location, scrolling distance, scrolling speed, etc., record the content entered and options selected by users in the form, listen for specific user behavior events, such as pop-up windows, video playback, etc., record the time users stay on the page, record the behavior of users jumping from one page to another. Before the user logs in or accesses the page, clearly inform the user of the purpose and method of behavior monitoring to ensure that the user has clearly agreed to the rules and methods of behavior monitoring when using the website or application. At the same time, as much as possible, anonymize the user data to avoid recording and saving information that can directly identify the user's identity, strengthen the security protection measures for user data, and avoid data leakage and abuse.

[0117] Step S43: Perform device screen adaptation on the device screen display interface according to the user usage feedback data, so as to generate an e-commerce platform hierarchical display interface.

[0118] In an embodiment of the present invention, by combining the results of previous A / B tests and user behavior monitoring data, user feedback data on the device screen display interface is collected. This includes user opinions and suggestions on interface layout, element size, font size, button position, etc. Use user feedback data for analysis to identify the most common problems, pain points and preferences encountered by users. This can be done through qualitative and quantitative analysis, such as user surveys, focus group discussions, text analysis and other methods. Based on the collected user feedback data, device screen adaptive design is performed. This means that the interface elements will be adjusted according to the screen size and resolution of different devices to ensure that a good user experience can be provided on various devices. Use technologies such as responsive design or fluid grid layout to enable the interface to automatically adapt to different screen sizes and maintain good usability and accessibility. Apply the designed adaptive interface to the e-commerce platform. This may involve cooperation between front-end developers and design teams to ensure that the interface can be presented well on various devices as expected, where specific device screen adaptation includes using percentages or relative units (such as em, rem) to define the width and spacing of page elements, so that the page can automatically adjust the layout according to the browser width, and apply different styles according to different screen sizes and resolutions. This can be achieved through CSS media queries, using max-width:100% to automatically scale images to different screen sizes, and using rem or em units to define font sizes to adapt to different devices. In the process of screen device adaptation, the user's personal privacy data will not be directly involved. The purpose of adaptive design is to provide a better user experience and does not involve the collection and processing of users' personal information.

[0119] In this specification, a hierarchical data display system based on an e-commerce platform is provided, which is used to execute the above-mentioned hierarchical data display method based on an e-commerce platform. The hierarchical data display system based on an e-commerce platform includes:

[0120] The initial interface construction module is used to obtain the structured data of the e-commerce platform; construct the initial display interface of the e-commerce platform without login based on the structured data of the e-commerce platform, and generate the initial display interface of the e-commerce platform;

[0121] The preference analysis module is used to obtain user login data; analyze the range of products that stay on the initial e-commerce platform display interface based on the user login data to obtain the data of products that stay; predict the types of products that users like based on the data of products that stay and generate prediction data of the types of products that users like; and adjust the order of recommended products on the initial e-commerce platform display interface based on the prediction data of the types of products that users like and generate a login e-commerce platform display interface;

[0122] An eye movement tracking module, which is used to analyze the user's attention area on the display interface of the landing e-commerce platform based on the device camera, and generate the user's attention distribution area; adjust the ambient perception brightness of the display interface of the landing e-commerce platform according to the user's attention distribution area, and generate the adjusted interface of the landing e-commerce platform;

[0123] A device compatibility module, which is used to make the adjusted interface of the landing e-commerce platform compatible with the device screen area, and generate the device screen display interface; conduct an A / B test on the device screen display interface and the adjusted interface of the landing e-commerce platform, and generate the display interface test data; adapt the adjusted interface of the landing e-commerce platform to the device screen through the display interface test data, so as to generate the hierarchical display interface of the e-commerce platform.

[0124] The beneficial effect of the present invention is that by generating a login e-commerce platform display interface based on user login data and favorite commodity type prediction, it is possible to present personalized commodity recommendations based on the user's interests and preferences, thereby improving the user's shopping experience and satisfaction. Based on the device camera, the user's attention area is analyzed, and the brightness is adjusted according to environmental perception, so that the display interface can be more in line with the user's visual habits, and the user's comfort and shopping experience on the platform are improved. The login e-commerce platform adjustment interface is subjected to device screen area compatibility and A / B testing to ensure that the interface presented on different devices can adapt to various screen sizes and resolutions, thereby improving the accessibility and coverage of the platform. Through the data analysis and A / B testing in steps S2 and S4, the user's behavior patterns and preference changes can be discovered in time, so that corresponding adjustments and optimizations can be made, and the display effect and user satisfaction of the e-commerce platform can be continuously improved. The personalized display and optimized interface can meet user needs more accurately, increase the user's click-through rate and willingness to buy recommended commodities, and thus improve the conversion rate and sales of the e-commerce platform. Therefore, the present invention improves the accuracy and real-time performance of hierarchical data display by constructing a hierarchical e-commerce display interface, tracking the user's eye movement and perceiving the user's attention, and displaying compatibility on different device screens. The initial interface construction module obtains structured data from the e-commerce platform, and then constructs an initial display interface based on these data. This interface is for non-logged-in users to see, and displays basic product categories, popular products, and other content, so that users can browse and search for products even when they are not logged in. The preference analysis module obtains the user's login data, including user search history, purchase history, browsing habits, etc., analyzes the user's behavior on the initial display interface, such as the dwell time, the product category clicked, etc., obtains the user's dwell product data, and uses machine learning or other algorithms to predict the user's preferences. It may be based on historical behavior to predict the type of product that the user may be interested in. According to the predicted user's preferred product type, the order of the products displayed on the initial interface is adjusted, so that the products seen by the logged-in user are more in line with his or her preferences. The eye tracking module tracks the user's eye movements through the device camera, analyzes the user's attention distribution on the page, and which products or areas the user looks at more. According to the user's attention distribution, the brightness of the page or other environmental perception factors are adjusted to improve the user experience and readability, and generate an adjusted interface to make users more comfortable and convenient when browsing products. The device compatibility module ensures that the adjusted interface of the login e-commerce platform can be well displayed on different devices without problems such as display dislocation or truncation. A / B testing is performed, that is, the adjusted interface is compared with the original interface, user feedback and data are collected, and the adjusted interface is further optimized based on the test data, so that the e-commerce platform can be displayed adaptively on different devices to improve user experience and accessibility.

[0125] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Accordingly, all changes that fall within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0126] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A hierarchical data display method based on an e-commerce platform, characterized in that, It includes the following steps: Step S1: Obtain the structured data of the e-commerce platform; Construct the initial display interface of the e-commerce platform without login for the structured data of the e-commerce platform, and generate the initial e-commerce platform display interface; Step S1 includes the following steps: Step S11: Obtain the structured data of the e-commerce platform, where the structured data of the e-commerce platform includes e-commerce texts and e-commerce images; Perform data preprocessing on the e-commerce texts to generate standard e-commerce text data, where the data preprocessing includes data cleaning, data missing value filling, and data standardization; perform image preprocessing on the e-commerce images to generate standard e-commerce images, where the image preprocessing includes image brightness enhancement, image geometric transformation, and image standardization; Construct the initial display interface of the e-commerce platform without login based on the standard e-commerce text data and standard e-commerce images, and generate the initial e-commerce platform display interface, where the initial e-commerce platform display interface includes a home page display interface, a search display interface, a product display interface, and a product settlement interface; the construction process of the home page display interface in Step S13 includes the following steps: Perform semantic association on the standard e-commerce text data and standard e-commerce images to obtain e-commerce product information data; Conduct sales analysis of the e-commerce product information data based on the e-commerce product information data to generate e-commerce product information sales data; sort the e-commerce product information data according to the e-commerce product information sales data for the priority of home page display to generate the initial e-commerce home page display interface; Conduct the same product brand style analysis on the e-commerce product information data to generate the same product brand style data; adjust the first order of product sorting for the initial e-commerce home page display interface according to the same product brand style data to generate the first order adjustment data of e-commerce home page products; Conduct product discount analysis on the e-commerce product information data based on the first order adjustment data of e-commerce home page products to generate product discount analysis data; adjust the second order of product sorting for the first order adjustment data of e-commerce home page products through the product discount analysis data to generate the second order adjustment data of the e-commerce home page; Dynamically adjust the initial e-commerce home page display interface without login by using the second order adjustment data of the e-commerce home page to generate the home page display interface; the construction process of the search display interface in Step S13 includes the following steps: Design a search box for the e-commerce product information data to obtain a product search box; extract the product attribute features from the e-commerce product information data to obtain e-commerce product attribute feature data, where the e-commerce product attribute feature data includes product name, product category, and product brand; Design a filter for the product search box based on the product name, product category, and product brand to obtain a product search filter; conduct the first round of search on the e-commerce product information data according to the product search filter to obtain the first round of product search display data; conduct the second round of search on the first round of product search display data according to the product search box to obtain the second round of product search display data; Perform product association connection on the product search display data of the first round and the product search display data of the second round to generate product association path data; design product cards for the e-commerce product information data according to the product path data to generate associated product cards; Perform dynamic loading of product search on the associated product cards based on the partial refresh technology to generate product search dynamic loading cards; use the product search filter and the product search box to sort the search results of the product search dynamic loading cards to generate e-commerce product search sorting data; Classify the e-commerce product search sorting data by product search priority to generate high-priority product search data and low-priority product search data; use the high-priority product search data and the low-priority product search data to adjust the search association path of the product association path data, so that the product association path data corresponding to the high-priority product search data is greater than the product association path data corresponding to the low-priority product search data, thereby generating a search display interface; Step S2: Obtain user login data; analyze the range of staying products on the initial e-commerce platform display interface based on the user login data to obtain staying product data; predict the types of products preferred by users for the staying product data to generate predicted data on the types of products preferred by users; adjust the sorting of recommended products on the initial e-commerce platform display interface through the predicted data on the types of products preferred by users to generate a logged-in e-commerce platform display interface; Step S3: Analyze the user attention area on the logged-in e-commerce platform display interface based on the device camera to generate the user attention distribution area; adjust the ambient perception brightness of the logged-in e-commerce platform display interface according to the user attention distribution area to generate an adjusted interface for the logged-in e-commerce platform; Step S4: Make the logged-in e-commerce platform adjusted interface compatible with the device screen area to generate a device screen display interface; conduct an A / B test on the device screen display interface and the logged-in e-commerce platform adjusted interface to generate display interface test data; adapt the device screen to the logged-in e-commerce platform adjusted interface through the display interface test data, thereby generating a hierarchical display interface for the e-commerce platform.

2. The hierarchical data display method based on an e-commerce platform according to claim 1, wherein The construction process of the product display interface in Step S13 includes the following steps: Perform product card layout on the e-commerce product information data based on the search display interface to generate e-commerce product card layout data, where the e-commerce product card layout data includes product pictures, product price data, product title data, and product rating data; Classify the product pictures by product type to generate product type data; construct a three-dimensional model based on the product type data to generate a three-dimensional model of the product type; simulate the scenario application of the e-commerce product according to the three-dimensional model of the product type to generate an e-commerce product application scenario; Import the e-commerce product card layout data into the e-commerce product application scenario for product attribute visualization to generate an e-commerce product virtual interaction scenario; display product information according to the e-commerce product virtual interaction scenario to generate e-commerce product virtual display information data; According to the virtual display information of e-commerce products, a review community is constructed for product price data, product title data and product rating data to generate e-commerce product review community data; the e-commerce product review community data and the e-commerce product virtual display information data are modularly integrated to generate a product display interface.

3. The hierarchical data display method based on an e-commerce platform according to claim 1, wherein The process of constructing the commodity settlement interface in step S13 includes the following steps: Confirm the product information display based on the product display interface and generate product information display confirmation data; select the product delivery address based on the product information display confirmation data and generate product delivery address filling data; select the product transportation method based on the product delivery address filling data and generate product transportation method selection data; Confirm the buyer and seller address information for the commodity transportation mode selection data to obtain the buyer's location information data and the seller's location information data; estimate the delivery time based on the buyer's location information data and the seller's location information data to generate the commodity's estimated delivery time; The commodity payment method is confirmed for the commodity information display confirmation data according to the estimated delivery time of the commodity to obtain the commodity payment method data; the commodity payment method data is encrypted for payment security to generate commodity payment encrypted data; the commodity order is confirmed based on the commodity payment encrypted data to generate commodity settlement data; the commodity settlement data is visualized to obtain the commodity settlement interface.

4. The hierarchical data display method based on an e-commerce platform according to claim 1, wherein Step S2 includes the following steps: Step S21: Obtain user login data; Step S22: acquiring the user's key-touching position information based on the user login data to obtain the user's key-touching position information data; analyzing the user's key-touching residence time on the user's key-touching position information data to generate the user's key-touching residence time data; Step S23: Analyze the range of products displayed on the initial e-commerce platform display interface according to the user's key touch stay time data to obtain stay product data; divide the stay product data into data sets to generate a model training set and a model test set; Step S24: using a convolutional neural network algorithm to perform model training on the model training set to generate a user-preferred product training model; performing model testing on the user-preferred product training model according to the model testing set to generate a user-preferred product prediction model; importing the stopped product data into the user-preferred product prediction model to predict the user-preferred product type and generate user-preferred product type prediction data; Step S25: adjusting the recommended product ranking of the initial e-commerce platform display interface based on the user's preferred product type prediction data, and generating a login e-commerce platform display interface.

5. The hierarchical data display method based on an e-commerce platform according to claim 1, wherein Step S3 includes the following steps: Step S31: collecting user eyeball images on the login e-commerce platform display interface based on the device camera to obtain user eyeball images; performing heat map analysis on the user eyeball images to generate user eyeball behavior data; Step S32: performing user eye tracking on the login e-commerce platform display interface according to the user eye behavior data to generate user eye tracking data, wherein the user eye tracking data includes user page gaze point, user scanning path data and user stay time data; Step S33: Analyze the user attention areas of the logged-in e-commerce platform display interface based on the user page fixation points, user saccade path data, and user dwell time data to generate the user attention distribution areas; use the user attention distribution areas to perform a waterfall flow adjustment of the products on the logged-in e-commerce platform display interface to generate a waterfall flow adjustment display mode for the products. Step S34: Detect the brightness of the eye color system of the user's eye image according to the waterfall flow adjustment display mode for the products to generate the ambient light perception brightness value of the eye; adjust the interface brightness of the logged-in e-commerce platform display interface through the ambient light perception brightness value of the eye to generate an adjusted interface for the logged-in e-commerce platform.

6. The hierarchical data display method based on an e-commerce platform according to claim 1, wherein Step S4 includes the following steps: Step S41: Analyze the device screen of the adjusted interface of the logged-in e-commerce platform to generate the display area of the device screen; cut the adjusted interface of the logged-in e-commerce platform according to the display area of the device screen to generate a display interface of the device screen. Step S42: Conduct an A / B test on the display interface of the device screen and the adjusted interface of the logged-in e-commerce platform to generate display interface test data; monitor the user behavior of the adjusted interface of the logged-in e-commerce platform through the display interface test data to generate user usage feedback data. Step S43: Adapt the device screen of the display interface of the device screen according to the user usage feedback data to generate a hierarchical display interface for the e-commerce platform.

7. A hierarchical data display system based on an e-commerce platform, characterized in that, For executing the hierarchical data display method based on an e-commerce platform as described in Claim 1, the hierarchical data display system based on an e-commerce platform includes: An initial interface construction module, configured to obtain the structured data of the e-commerce platform; construct an initial display interface for the non-logged-in e-commerce platform for the structured data of the e-commerce platform to generate an initial display interface for the e-commerce platform. A preference analysis module, configured to obtain user login data; analyze the range of products stayed on the initial display interface of the e-commerce platform based on the user login data to obtain the data of the products stayed; predict the types of products preferred by the user for the data of the products stayed to generate prediction data of the types of products preferred by the user; adjust the sorting of the recommended products on the initial display interface of the e-commerce platform through the prediction data of the types of products preferred by the user to generate a display interface for the logged-in e-commerce platform. An eye movement tracking module, configured to analyze the user attention areas of the logged-in e-commerce platform display interface based on the device camera to generate the user attention distribution areas; adjust the ambient light perception brightness of the logged-in e-commerce platform display interface according to the user attention distribution areas to generate an adjusted interface for the logged-in e-commerce platform. A device compatibility module, configured to make the device screen area of the adjusted interface of the logged-in e-commerce platform compatible to generate a display interface of the device screen; conduct an A / B test on the display interface of the device screen and the adjusted interface of the logged-in e-commerce platform to generate display interface test data; adapt the device screen of the adjusted interface of the logged-in e-commerce platform through the display interface test data to generate a hierarchical display interface for the e-commerce platform.

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