A full-link user behavior data collection and hierarchical interaction analysis system and method thereof

Abstract

The application relates to a full-link user behavior data collection and hierarchical interaction analysis system and method, belongs to the technical field of user big data collection and intelligent analysis, and aims to solve the problems that most existing user behavior data collection and hierarchical interaction analysis systems only perform simple burying point collection on single terminals, the existing user hierarchical mode is relatively rough, and user behavior mining mostly adopts a complex deep learning algorithm; the application realizes full-channel full-quantity behavior data collection, collects core behavior data in full quantity, discards a traditional static attribute hierarchical mode, constructs a three-level hierarchical system based on real life cycle behaviors of user access, trial and payment, adopts a light-weight CNN-LSTM hybrid model matched with a standardized quantification formula, avoids complex algorithm power consumption, can accurately quantize user activity, conversion tendency and loss risk, clearly mines user behavior rules at all levels, and all parameters can be interpreted, quantified and debugged, and the analysis result is objective and accurate.

Description

Technical Field

[0001] This invention relates to the field of user big data collection and intelligent analysis technology, and in particular to a system for collecting and analyzing end-to-end user behavior data and hierarchical interactive analysis. Background Technology

[0002] With the continuous upgrading of the demand for refined internet operations and product iteration, intelligent analysis based on user behavior data has become a core means of product optimization, user operation, and conversion rate improvement. However, current mainstream user behavior analysis technologies have many shortcomings, which seriously restrict the effectiveness of refined operations.

[0003] Most existing user behavior data collection and hierarchical interaction analysis systems only perform simple data collection on a single terminal, failing to achieve full coverage of web pages, apps, mini-programs, and multiple channels. Furthermore, existing user segmentation methods are rather crude, relying primarily on static attributes such as age, region, and spending amount, without considering the dynamic behavioral characteristics of the entire user lifecycle (access, trial, payment). This makes it difficult to accurately distinguish between potential users, active users, and valuable users. Additionally, existing user behavior mining often employs complex deep learning algorithms, requiring high computing power and incurring high deployment costs. It also focuses on data fitting, lacking quantitative pattern mining tailored to business needs, and cannot accurately output actionable directions for product iteration and operational optimization, exhibiting a problem of "heavy on algorithms, light on implementation."

[0004] To address the aforementioned issues, a system for collecting and analyzing end-to-end user behavior data and employing hierarchical interaction is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a system and method for full-link user behavior data collection and hierarchical interaction analysis. This addresses the shortcomings of existing user behavior data collection and hierarchical interaction analysis systems, which mostly only perform simple data collection on a single terminal, failing to achieve full coverage of web pages, apps, mini-programs, and multiple channels. Furthermore, existing user segmentation methods are relatively crude, often based on static attributes such as age, region, and spending amount, without considering the dynamic behavioral characteristics of the entire user lifecycle (access, trial, payment), making it difficult to accurately distinguish between potential users, active users, and valuable users. Additionally, existing user behavior mining often employs complex deep learning algorithms, requiring high computing power and incurring high deployment costs. It also focuses on data fitting, lacking quantitative pattern mining tailored to business needs, and cannot accurately output actionable product iteration and operational optimization directions, exhibiting a problem of "heavy on algorithms, light on implementation."

[0006] To achieve the above objectives, the present invention provides the following technical solution: a full-link user behavior data collection and hierarchical interactive analysis system, comprising a multi-channel full-volume behavior tracking module, a data preprocessing and standardization module, a user lifecycle hierarchical module, a deep learning behavior pattern mining module, and an intelligent analysis output module; The multi-channel full-volume behavior tracking module is used to connect with multiple terminal channels such as web pages, mobile apps, WeChat mini programs, and H5 pages, deploy a unified adaptive tracking script, and collect raw behavior data of user page click behavior, page dwell time, and interactive comment behavior, and simultaneously bind user unique identifier, terminal identifier, access time, and page path related dimension data. The data preprocessing and standardization module is communicatively connected to the multi-channel full-volume behavior tracking module. It is used to receive raw behavior data from multiple channels, complete data deduplication, missing value repair, abnormal data removal, and format standardization processing to generate a structured standard user behavior dataset. The user lifecycle layering module is communicatively connected to the data preprocessing and standardization module. It is used to construct a three-level user layering system based on the structured standard user behavior dataset and the entire user lifecycle of access, trial, and payment as the layering dimension, dividing users into potential users in the access layer, active users in the trial layer, and valuable users in the payment layer. The deep learning behavior pattern mining module is communicatively connected to the user lifecycle hierarchical module. It is used to receive structured behavior data of users at each level, and through a lightweight time-series deep learning model, quantitatively mine the behavioral preferences, access patterns, conversion characteristics, and churn risk patterns of users at each level, and output quantitative parameters of user behavior characteristics at each level. The intelligent analysis output module is communicatively connected to the deep learning behavior pattern mining module. It is used to intelligently match product iteration optimization rules and operation optimization rules based on the user behavior pattern mining results at each level, and output a visualized product iteration plan and a refined operation optimization direction report.

[0007] Furthermore, the multi-channel full-volume behavior tracking module includes a terminal adaptation tracking sub-module, a behavior classification and collection sub-module, and a real-time data reporting sub-module; The terminal adaptation tracking submodule is used to adaptively configure non-intrusive tracking scripts for page rendering rules of different terminal channels, supporting full coverage tracking of dynamic pages, static pages, and pop-up components without modifying the terminal business code. The behavior classification and collection submodule is used to classify and collect three types of core behavior data: page click behavior data, including click location, click component, click time, and click frequency; page dwell time data, including single-page dwell time, total dwell time in a single session, and cumulative dwell time across multiple pages; and interactive message behavior data, including message content, message time, number of messages, and interactive feedback type. The real-time data reporting submodule is used to combine batch delayed reporting with real-time key data reporting. It reports ordinary click and dwell data in batches at regular intervals, and reports core data of message interaction in real time, ensuring data integrity while reducing terminal resource consumption.

[0008] Furthermore, the data preprocessing and standardization module includes a data cleaning submodule, a data normalization submodule, and a time-series alignment submodule; The data cleaning submodule is used to remove redundant data collected repeatedly, invalid data that has timed out, and abnormal extreme value data, and to complete valid data with missing user identifiers and timestamps. The data normalization submodule is used to convert behavioral data from different terminals and in different formats into a standard structured data format, and to unify the measurement dimensions of duration, frequency, and number of times. The time-series alignment submodule is used to sort and align scattered click, dwell, and comment behavior data in time sequence on a single user access session basis, and construct a complete time sequence of user session-level behavior.

[0009] Furthermore, the user lifecycle stratification module is configured with a lifecycle stratification determination threshold system, the stratification system being as follows: Potential users at the access layer: Users who only visit pages and click to browse, but do not try out product features or make any payments. Trial-level active users: Users who have completed the trial of the core functions of the product, generated active interactive comments, but have not made any paid transactions; Paid tier value users: Users who have completed any one of the following paid behaviors: product purchase, renewal, or consumption of value-added services; The user lifecycle layering module is also used to update the user level status in real time and realize dynamic upgrading and downgrading of the level based on the user's subsequent behavior.

[0010] Furthermore, the deep learning behavior pattern mining module adopts a lightweight temporal CNN-LSTM hybrid model to extract user temporal behavior features. The model includes a behavior feature extraction layer, a temporal correlation layer, and a pattern classification output layer. The model is configured with standardized quantization calculation formulas to quantify user behavior features at each level.

[0011] Another technical solution proposed in this invention: A method for a full-link user behavior data collection and hierarchical interaction analysis system, comprising the following steps: S1. Multi-channel full-volume behavior tracking data collection: Through adaptive unified tracking scripts, it connects to multiple terminal channels to collect all user page clicks, dwell time, and interactive comments, binds user-related dimension data, and reports in batches in real time. S2. Data preprocessing standardization: The reported raw behavior data is cleaned, normalized, and time-series aligned to generate a session-level structured standard user behavior dataset. S3. User Lifecycle Layering: Based on standardized behavioral datasets, users are divided into three layers—access layer, trial layer, and payment layer—according to their access, trial, and payment behavior characteristics, and the user layer is dynamically updated. S4. Deep Learning Behavioral Pattern Mining: A lightweight time-series deep learning model is used to input time-series behavioral data of users at each level. The pre-set algorithm is used to quantitatively mine the behavioral preferences, conversion patterns, and churn patterns of users at each level. S5. Intelligent Output Optimization Solution: Based on the results of mining user behavior patterns at each level, specific optimization directions and implementation strategies are generated for product feature iteration, page layout optimization, and refined user operation.

[0012] Furthermore, in step S4, the behavioral feature quantification calculation formula of the lightweight temporal deep learning model includes the user behavior activity calculation formula, the user conversion tendency calculation formula, and the user churn risk calculation formula. The formula for calculating user behavior activity is as follows: ; in, This represents the user's current activity level. This refers to the frequency of valid clicks by users per unit of time. This refers to the average time a user spends on a page per unit of time. This refers to the number of user interactions and comments within a unit of time. These are the weight coefficients for the corresponding dimensions, and The values ​​range from 0 to 1; The formula for calculating user conversion propensity is: ; in, To increase user willingness to pay. To measure the completion rate of users' core function trials, The interval between the user's last access. To correspond with the corrected weighting coefficients, the values ​​range from 0 to 1; The formula for calculating the risk of user churn is as follows: ; in, To assess the risk of user churn, The percentage of consecutive days without user activity. These are risk weighting coefficients, with values ​​ranging from 0 to 1.

[0013] Furthermore, the specific process of deep learning pattern mining in step S4 includes: S41. Construct behavioral feature vectors from the standardized temporal behavioral data of users at each level and input them into a lightweight temporal CNN-LSTM hybrid model. S42. Extract local user behavior features through CNN convolutional layers, including high-frequency click components, core dwell pages, and key interactive content; S43. Capture long-term behavioral change patterns of users through the LSTM time-series memory layer to identify fluctuations in user behavior trends; S44. Combine quantitative calculation formulas to output quantitative results of user activity, conversion tendency, and churn risk at each level; S45. Based on the quantitative results, cluster and divide users at each level into subdivided behavioral groups to explore the core behavioral characteristics and differentiated needs of different groups.

[0014] Furthermore, the specific logic of the intelligent output optimization scheme in S5 includes: For potential users at the access layer: Based on users' frequently viewed pages and click preferences, output optimization plans for page layout, homepage content iteration, and new user acquisition and operation optimization; For active trial users: Based on user feedback on feature limitations, interactive messages, and conversion bottlenecks, we will develop strategies to optimize core product features, simplify the trial process, and guide conversion. For high-value paying users: Based on user payment preferences, churn risk, and feature usage needs, we will provide solutions for value-added feature iterations, membership benefit optimization, and high-value user retention and operation.

[0015] Furthermore, the method also includes data iteration and model optimization steps: periodically collecting new user behavior data, updating user levels and behavior pattern mining results, and fine-tuning the weight coefficients and stratification thresholds of the deep learning model based on the historical optimization results, so as to achieve continuous iteration and improvement of analysis accuracy.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention provides a full-link user behavior data collection and hierarchical interaction analysis system and method, which realizes the collection of full-volume behavior data across all channels with high data integrity, covering web pages, apps, mini-programs, and H5 terminals. Through adaptive non-intrusive tracking technology, it collects three core types of user behavior data: page clicks, dwell time, and interactive comments. Combined with a hybrid reporting mechanism, it completely solves the problems of single data collection channels, missing data dimensions, and incomplete data in existing technologies. It fully restores the user's product usage behavior across the entire link and solves the problem that most existing user behavior data collection and hierarchical interaction analysis systems only perform simple tracking data collection for a single terminal and cannot achieve full coverage of web pages, apps, mini-programs, and multiple channels.

[0017] 2. This invention provides a full-link user behavior data collection and hierarchical interactive analysis system and method. By abandoning the traditional static attribute hierarchical model, it constructs a three-level hierarchical system based on the real lifecycle behavior of users' access, trial, and payment. It supports dynamic upgrading and downgrading of user levels, accurately distinguishes potential, active, and valuable users, and the hierarchical results are fully aligned with product operation and iteration business scenarios. This achieves precise user hierarchical alignment with business needs and is highly dynamic. It solves the problem that existing user behavior data collection and hierarchical interactive analysis systems have relatively crude user hierarchical methods, which are mostly based on static attributes such as user age, region, and consumption amount, without combining the dynamic behavioral characteristics of the complete lifecycle of user access, trial, and payment, and thus cannot accurately distinguish potential users, active users, and valuable users.

[0018] 3. This invention provides a full-link user behavior data collection and hierarchical interaction analysis system and method. By employing a lightweight CNN-LSTM hybrid model combined with standardized quantization formulas, it avoids the computational cost of complex algorithms. It can accurately quantify user activity, conversion tendency, and churn risk, clearly uncovering user behavior patterns at each level. All parameters are interpretable, quantifiable, and debuggable, and the analysis results are objective and accurate. This solves the problem that existing user behavior data collection and hierarchical interaction analysis systems often use complex deep learning algorithms for user behavior mining, which have high computational requirements, high deployment costs, and focus on data fitting, lacking quantitative pattern mining that fits the business, and cannot accurately output feasible product iteration and operational optimization directions, resulting in the problem of "heavy on algorithms, light on implementation". Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] To address the shortcomings of existing user behavior data collection and layered interaction analysis systems, which mostly rely on simple data collection on single terminals and fail to achieve full coverage across web pages, apps, mini-programs, and multiple channels, and whose current user segmentation methods are often crude, relying primarily on static attributes such as age, region, and spending amount without considering the dynamic behavioral characteristics of the entire user lifecycle (access, trial, payment), they cannot accurately distinguish between potential users, active users, and valuable users. Furthermore, existing user behavior mining often employs complex deep learning algorithms, requiring high computing power and incurring high deployment costs. It also focuses on data fitting, lacking quantitative pattern discovery tailored to business needs, and cannot accurately output actionable directions for product iteration and operational optimization. This results in a technical problem of "heavy on algorithms, light on implementation," such as... Figure 1 As shown, the following preferred technical solutions are provided: A full-link user behavior data collection and hierarchical interactive analysis system includes a multi-channel full-volume behavior tracking module, a data preprocessing and standardization module, a user lifecycle hierarchical module, a deep learning behavior pattern mining module, and an intelligent analysis output module. The multi-channel full-volume behavior tracking module connects to multiple terminal channels such as web pages, mobile apps, WeChat mini-programs, and H5 pages, deploys a unified adaptive tracking script, and collects raw behavioral data on user page clicks, page dwell time, and interactive comments, synchronously binding user unique identifiers, terminal identifiers, access time, and page path-related dimension data. The data preprocessing and standardization module communicates with the multi-channel full-volume behavior tracking module to receive raw behavioral data from multiple channels, performing data deduplication, missing value repair, abnormal data removal, and format standardization to generate a structured standard user behavior dataset. The user lifecycle... The system comprises three modules: a tiered user segmentation module and a communication connection data preprocessing and standardization module. The former is used to construct a three-tiered user segmentation system based on a structured standard user behavior dataset, using the entire user lifecycle (access, trial, payment) as the tiering dimension. Users are divided into three tiers: potential users (access layer), active users (trial layer), and valuable users (paying layer). The latter is a deep learning behavior pattern mining module. This module receives structured behavior data from each tier and uses a lightweight time-series deep learning model to quantitatively mine user behavior preferences, access patterns, conversion characteristics, and churn risk patterns at each tier, outputting quantitative parameters of user behavior characteristics for each tier. The final module, an intelligent analysis output module, is used to intelligently match product iteration optimization rules and operational optimization rules based on the results of user behavior pattern mining at each tier, outputting a visualized product iteration plan and a refined operational optimization direction report.

[0022] The overall architecture realizes a multi-channel, full-volume three-category core behavior data collection architecture, a dynamic layered system for the access-trial-payment lifecycle, a lightweight and quantifiable deep learning behavior mining model, and a closed-loop output logic from data to product operation optimization.

[0023] The multi-channel full-volume behavior tracking module includes a terminal adaptation tracking submodule, a behavior classification collection submodule, and a real-time data reporting submodule. The terminal adaptation tracking submodule is used to adaptively configure non-intrusive tracking scripts based on page rendering rules for different terminal channels, supporting full coverage tracking of dynamic pages, static pages, and pop-up components without modifying terminal business code. The behavior classification collection submodule is used to collect three types of core behavior data: page click behavior data including click location, click component, click time, and click frequency; page dwell time data including single-page dwell time, total dwell time in a single session, and cumulative dwell time across multiple pages; and interactive message behavior data including message content, message time, number of messages, and interaction feedback type. The real-time data reporting submodule uses a combination of batch delayed reporting and real-time key data reporting to batch report ordinary click and dwell data on a scheduled basis, and to report core interactive data in real time, ensuring data integrity while reducing terminal resource consumption.

[0024] The data preprocessing and standardization module includes a data cleaning submodule, a data normalization submodule, and a time-series alignment submodule. The data cleaning submodule is used to remove redundant data collected repeatedly, invalid data that has timed out, and abnormal extreme value data, and to complete valid data with missing user identifiers and timestamps. The data normalization submodule is used to convert behavioral data from different terminals and in different formats into a standardized structured data format, and to unify the measurement dimensions of duration, frequency, and number of times. The time-series alignment submodule is used to sort and align scattered click, dwell, and comment behavior data in time sequence at the user's single access session level, and to construct a complete behavioral time sequence at the user session level.

[0025] The user lifecycle stratification module is configured with a lifecycle stratification determination threshold system, which is as follows: Potential users at the access layer: Users who only visit pages and click to browse, but do not try out product features or make any payments. Trial-level active users: Users who have completed the trial of the core functions of the product, generated active interactive comments, but have not made any paid transactions; Paid tier value users: Users who have completed any one of the following paid behaviors: product purchase, renewal, or consumption of value-added services; The user lifecycle layering module is also used to update the user level status in real time and dynamically upgrade or downgrade the level based on the user's subsequent behavior.

[0026] The deep learning behavior pattern mining module adopts a lightweight temporal CNN-LSTM hybrid model to extract user temporal behavior features. The model includes a behavior feature extraction layer, a temporal correlation layer, and a pattern classification output layer. The model is configured with standardized quantization calculation formulas to quantify user behavior features at each level.

[0027] To further illustrate the above embodiments, the present invention also provides an implementation scheme, a method for a full-link user behavior data collection and hierarchical interaction analysis system, comprising the following steps: Step 1: Multi-channel full-volume behavior tracking data collection: Through adaptive unified tracking scripts, connect to multiple terminal channels to collect all user page clicks, dwell time, and interactive comments, bind user-related dimension data, and report in batches in real time; Step 2, Data Preprocessing and Standardization: The reported raw behavioral data is cleaned, normalized, and time-series aligned to generate a session-level structured standard user behavior dataset. Step 3: User lifecycle segmentation: Based on standardized behavioral datasets, users are divided into three levels—access layer, trial layer, and payment layer—according to their access, trial, and payment behavior characteristics, and the user levels are dynamically updated. Step 4: Deep Learning Behavioral Pattern Mining: Using a lightweight temporal deep learning model, input the temporal behavioral data of users at each level, and use a preset algorithm to quantitatively mine the behavioral preferences, conversion patterns, and churn patterns of users at each level; Step 5: Intelligent Output Optimization Plan: Based on the results of user behavior pattern mining at each level, generate specific optimization directions and implementation strategies for product function iteration, page layout optimization, and refined user operation.

[0028] The behavioral feature quantification calculation formulas for the lightweight temporal deep learning model in step four include the user behavior activity calculation formula, the user conversion tendency calculation formula, and the user churn risk calculation formula. The formula for calculating user activity is: ; in, This represents the user's current activity level. This refers to the frequency of valid clicks by users per unit of time. This refers to the average time a user spends on a page per unit of time. This refers to the number of user interactions and comments within a unit of time. These are the weight coefficients for the corresponding dimensions, and The values ​​range from 0 to 1; The formula for calculating user conversion propensity is: ; in, To increase user willingness to pay. To measure the completion rate of users' core function trials, The interval between the user's last access. To correspond with the corrected weighting coefficients, the values ​​range from 0 to 1; The formula for calculating the risk of user churn is: ; in, To assess the risk of user churn, The percentage of consecutive days without user activity. These are risk weighting coefficients, with values ​​ranging from 0 to 1.

[0029] The specific process of deep learning pattern discovery in step four includes: The first step is to construct behavioral feature vectors from the standardized temporal behavioral data of users at each level and input them into a lightweight temporal CNN-LSTM hybrid model. The second step is to extract local user behavior features through CNN convolutional layers, including high-frequency click components, core pages where users stay, and key interactive content. The third step is to capture the long-term behavioral change patterns of users through the LSTM time-series memory layer and identify fluctuations in user behavior trends. The fourth step is to combine quantitative calculation formulas to output quantitative results of user activity, conversion tendency, and churn risk at each level; Step 5: Based on the quantitative results, cluster and segment users at each level to identify their specific behavioral groups and uncover the core behavioral characteristics and differentiated needs of different groups.

[0030] The specific logic of the intelligent output optimization scheme in step five includes: For potential users at the access layer: Based on users' frequently viewed pages and click preferences, output optimization plans for page layout, homepage content iteration, and new user acquisition and operation optimization; For active trial users: Based on user feedback on feature limitations, interactive messages, and conversion bottlenecks, we will develop strategies to optimize core product features, simplify the trial process, and guide conversion. For high-value paying users: Based on user payment preferences, churn risk, and feature usage needs, we will provide solutions for value-added feature iterations, membership benefit optimization, and high-value user retention and operation.

[0031] The method of the end-to-end user behavior data collection and hierarchical interactive analysis system also includes data iteration and model optimization steps: periodically collect new user behavior data, update user hierarchy and behavior pattern mining results, and fine-tune the weight coefficients and hierarchical judgment thresholds of the deep learning model based on the historical optimization implementation effect to achieve continuous iteration and improvement of analysis accuracy.

[0032] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0033] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A system for collecting and analyzing end-to-end user behavior data and hierarchical interaction, characterized in that: It includes a multi-channel full-volume behavior tracking module, a data preprocessing and standardization module, a user lifecycle layering module, a deep learning behavior pattern mining module, and an intelligent analysis output module; The multi-channel full-volume behavior tracking module is used to connect with multiple terminal channels such as web pages, mobile apps, WeChat mini programs, and H5 pages, deploy a unified adaptive tracking script, and collect raw behavior data of user page click behavior, page dwell time, and interactive comment behavior, and simultaneously bind user unique identifier, terminal identifier, access time, and page path related dimension data. The data preprocessing and standardization module is communicatively connected to the multi-channel full-volume behavior tracking module. It is used to receive raw behavior data from multiple channels, complete data deduplication, missing value repair, abnormal data removal, and format standardization processing to generate a structured standard user behavior dataset. The user lifecycle layering module is communicatively connected to the data preprocessing and standardization module. It is used to construct a three-level user layering system based on the structured standard user behavior dataset and the entire user lifecycle of access, trial, and payment as the layering dimension, dividing users into potential users in the access layer, active users in the trial layer, and valuable users in the payment layer. The deep learning behavior pattern mining module is communicatively connected to the user lifecycle hierarchical module. It is used to receive structured behavior data of users at each level, and through a lightweight time-series deep learning model, quantitatively mine the behavioral preferences, access patterns, conversion characteristics, and churn risk patterns of users at each level, and output quantitative parameters of user behavior characteristics at each level. The intelligent analysis output module is communicatively connected to the deep learning behavior pattern mining module. It is used to intelligently match product iteration optimization rules and operation optimization rules based on the user behavior pattern mining results at each level, and output a visualized product iteration plan and a refined operation optimization direction report.

2. The end-to-end user behavior data collection and hierarchical interaction analysis system as described in claim 1, characterized in that: The multi-channel full-volume behavior tracking module includes a terminal adaptation tracking sub-module, a behavior classification collection sub-module, and a real-time data reporting sub-module. The terminal adaptation tracking submodule is used to adaptively configure non-intrusive tracking scripts for page rendering rules of different terminal channels, supporting full coverage tracking of dynamic pages, static pages, and pop-up components without modifying the terminal business code. The behavior classification and collection submodule is used to classify and collect three types of core behavior data: page click behavior data, including click location, click component, click time, and click frequency; Page dwell time data includes single-page dwell time, total dwell time in a single session, and cumulative dwell time across multiple pages; Interactive commenting behavior data includes comment content, comment time, number of comments, and type of interactive feedback; The real-time data reporting submodule is used to adopt a combination of batch delayed reporting and real-time key data reporting mode to batch report ordinary click and dwell data on a timed basis, and to report core data of message interaction in real time, so as to ensure data integrity while reducing terminal resource consumption.

3. The end-to-end user behavior data collection and hierarchical interaction analysis system as described in claim 2, characterized in that: The data preprocessing and standardization module includes a data cleaning submodule, a data normalization submodule, and a time-series alignment submodule. The data cleaning submodule is used to remove redundant data collected repeatedly, invalid data that has timed out, and abnormal extreme value data, and to complete valid data with missing user identifiers and timestamps. The data normalization submodule is used to convert behavioral data from different terminals and in different formats into a standard structured data format, and to unify the measurement dimensions of duration, frequency, and number of times. The time-series alignment submodule is used to sort and align scattered click, dwell, and comment behavior data in time sequence on a single user access session basis, and construct a complete time sequence of user session-level behavior.

4. The end-to-end user behavior data collection and hierarchical interaction analysis system as described in claim 3, characterized in that: The user lifecycle stratification module is configured with a lifecycle stratification determination threshold system, which is specifically as follows: Potential users at the access layer: Users who only visit pages and click to browse, but do not try out product features or make any payments. Trial-level active users: Users who have completed the trial of the core functions of the product, generated active interactive comments, but have not made any paid transactions; Paid tier value users: Users who have completed any one of the following paid behaviors: product purchase, renewal, or consumption of value-added services; The user lifecycle layering module is also used to update the user level status in real time and realize dynamic upgrading and downgrading of the level based on the user's subsequent behavior.

5. The end-to-end user behavior data collection and hierarchical interaction analysis system as described in claim 4, characterized in that: The deep learning behavior pattern mining module adopts a lightweight temporal CNN-LSTM hybrid model to extract user temporal behavior features. The model includes a behavior feature extraction layer, a temporal correlation layer, and a pattern classification output layer. The model is configured with standardized quantization calculation formulas to quantify user behavior features at each level.

6. A method for a full-link user behavior data collection and hierarchical interaction analysis system as described in claim 5, characterized in that, Includes the following steps: S1. Multi-channel full-volume behavior tracking data collection: Through adaptive unified tracking scripts, it connects to multiple terminal channels to collect all user page clicks, dwell time, and interactive comments, binds user-related dimension data, and reports in batches in real time. S2. Data preprocessing standardization: The reported raw behavior data is cleaned, normalized, and time-series aligned to generate a session-level structured standard user behavior dataset. S3. User Lifecycle Layering: Based on standardized behavioral datasets, users are divided into three layers—access layer, trial layer, and payment layer—according to their access, trial, and payment behavior characteristics, and the user layer is dynamically updated. S4. Deep Learning Behavioral Pattern Mining: A lightweight time-series deep learning model is used to input time-series behavioral data of users at each level. The pre-set algorithm is used to quantitatively mine the behavioral preferences, conversion patterns, and churn patterns of users at each level. S5. Intelligent Output Optimization Solution: Based on the results of mining user behavior patterns at each level, specific optimization directions and implementation strategies are generated for product feature iteration, page layout optimization, and refined user operation.

7. A method for a full-link user behavior data collection and hierarchical interaction analysis system as described in claim 6, characterized in that, In step S4, the formulas for quantifying the behavioral features of the lightweight temporal deep learning model include the formulas for calculating user behavior activity, user conversion tendency, and user churn risk. The formula for calculating user behavior activity is as follows: ; in, This represents the user's current activity level. This refers to the frequency of valid clicks by users per unit of time. This refers to the average time a user spends on a page per unit of time. This refers to the number of user interactions and comments within a unit of time. These are the weight coefficients for the corresponding dimensions, and The values ​​range from 0 to 1; The formula for calculating user conversion propensity is as follows: ; in, To increase user willingness to pay. To measure the completion rate of users' core function trials, The interval between the user's last access. To correspond with the corrected weighting coefficients, the values ​​range from 0 to 1; The formula for calculating the risk of user churn is as follows: ; in, To assess the risk of user churn, The percentage of consecutive days without user activity. These are risk weighting coefficients, with values ​​ranging from 0 to 1.

8. A method for a full-link user behavior data collection and hierarchical interaction analysis system as described in claim 7, characterized in that, The specific process of deep learning pattern mining in step S4 includes: S41. Construct behavioral feature vectors from the standardized temporal behavioral data of users at each level and input them into a lightweight temporal CNN-LSTM hybrid model. S42. Extract local user behavior features through CNN convolutional layers, including high-frequency click components, core dwell pages, and key interactive content; S43. Capture long-term behavioral change patterns of users through the LSTM time-series memory layer to identify fluctuations in user behavior trends; S44. Combine quantitative calculation formulas to output quantitative results of user activity, conversion tendency, and churn risk at each level; S45. Based on the quantitative results, cluster and divide users at each level into subdivided behavioral groups to explore the core behavioral characteristics and differentiated needs of different groups.

9. A method for a full-link user behavior data collection and hierarchical interaction analysis system as described in claim 8, characterized in that, The specific logic of the intelligent output optimization scheme in S5 includes: For potential users at the access layer: Based on users' frequently viewed pages and click preferences, output optimization plans for page layout, homepage content iteration, and new user acquisition and operation optimization; For active trial users: Based on user feedback on feature limitations, interactive messages, and conversion bottlenecks, we will develop strategies to optimize core product features, simplify the trial process, and guide conversion. For high-value paying users: Based on user payment preferences, churn risk, and feature usage needs, we will provide solutions for value-added feature iterations, membership benefit optimization, and high-value user retention and operation.

10. A method for a full-link user behavior data collection and hierarchical interaction analysis system as described in claim 9, characterized in that, The method also includes data iteration and model optimization steps: periodically collect new user behavior data, update user levels and behavior pattern mining results, and fine-tune the weight coefficients and stratification thresholds of the deep learning model based on the historical optimization results to achieve continuous iteration and improvement of analysis accuracy.

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