E-commerce competition violation identification system based on distributed crawler and semantic analysis

Abstract

The application provides an e-commerce competition violation identification system based on a distributed crawler and semantic analysis, which comprises a distributed crawler subsystem and an intelligent semantic analysis subsystem; the distributed crawler subsystem constructs structured configuration information and periodically collects structured data of an e-commerce platform; the intelligent semantic analysis subsystem receives the structured data, performs text preprocessing, basic semantic feature extraction, deep semantic feature extraction, semantic similarity retrieval, and flexible violation joint discrimination; the flexible violation joint discrimination determines the context correction and progressively discriminates the flexible violation behavior; the application can realize full-channel high-concurrency data collection of e-commerce platform data, accurately identify ambiguous violation semantics, improve the identification accuracy of flexible violation behavior, and construct a complete and reliable evidence chain.

Description

Technical Field

[0001] This invention belongs to the field of Internet big data monitoring and natural language processing technology, specifically relating to an e-commerce competition violation identification system based on distributed crawling and semantic analysis. Background Technology

[0002] Existing e-commerce competition violation detection technologies have the following significant shortcomings: First, data collection efficiency is low. Information on competition violations is scattered across multiple e-commerce platforms and channels, limiting the collection rate and making continuous and stable collection difficult.

[0003] Second, the accuracy of violation identification is insufficient. Existing technologies mainly rely on keyword matching to filter illegal content. However, the detection rate of keyword matching is extremely low when faced with subtle violations such as veiled expressions, semantic distortions, and insinuations. Although some patent documents have proposed intelligent web crawler security collection methods based on dynamic feature recognition, they only involve web crawler technology and fail to solve the problem of semantic-level violation identification. Other patent documents achieve deep semantic violation identification through metaphor word recognition or use cross-modal attention mechanisms for false advertising detection, but these are all for real-time data processing in live streaming scenarios.

[0004] Third, the chain of evidence is incomplete. Existing technologies are mostly fragmented architectures that separate collection, identification, and evidence storage. There is a lack of traceable and credible connection between the violation identification results and the original collected data, making it difficult to serve as a reliable basis for law enforcement and platform governance.

[0005] In summary, there is an urgent need to address the problems of low data collection efficiency and susceptibility to blocking on e-commerce platforms, inability to parse obscure and illegal semantics, low accuracy in identifying subtle violations, and incomplete evidence chains. Summary of the Invention

[0006] This invention proposes an e-commerce competition violation identification system based on distributed crawling and semantic analysis. It can achieve high-concurrency data collection from all channels of e-commerce platforms, accurately identify obscure violation semantics, improve the accuracy of identifying subtle violations, and construct a complete and credible chain of evidence.

[0007] To achieve the above objectives, the technical solution of the present invention is implemented as follows: An e-commerce competition violation identification system based on distributed crawling and semantic analysis includes a distributed crawling subsystem and an intelligent semantic analysis subsystem; The distributed crawler subsystem constructs structured configuration information for each e-commerce platform, uses a Master-Worker architecture for crawler task scheduling, provides different proxy strategies for different e-commerce platforms based on a dynamic proxy pool, and periodically collects structured data from the e-commerce platforms. The intelligent semantic analysis subsystem receives structured data from the e-commerce platform collected by the distributed crawler subsystem, performs text preprocessing, basic semantic feature extraction, deep semantic feature extraction, and semantic similarity retrieval, and performs flexible violation joint judgment; the flexible violation joint judgment progressively judges flexible violation behaviors through context-corrected adjudication.

[0008] Furthermore, the distributed crawler subsystem includes: A multi-platform compatible crawler manager that builds and maintains structured configuration information for various e-commerce platforms; The distributed task scheduling unit adopts a Master-Worker architecture for crawler task distribution and load balancing. The dynamic proxy pool unit maintains IP proxy resources and provides proxy distribution strategies for different e-commerce platforms. The structured data parsing unit extracts structured data, including product titles, details text, and advertising copy, from the acquired e-commerce platform pages; Incremental data collection triggers are configured to re-capture the data periodically based on the average update frequency of product information on each platform, thereby periodically updating the structured data of the e-commerce platform.

[0009] Preferably, the distributed task scheduling unit maintains a queue of tasks to be crawled, a queue of tasks in progress, and a queue of tasks completed, and schedules tasks based on priority coefficients. Sort the tasks. ; in: As a time-sensitive factor, ; Indicates the current timestamp. Indicates the product's listing timestamp. Indicates the preset time window length; Platform weight reflects the importance or priority of different e-commerce platforms in the violation monitoring task. ; This is the historical success rate factor; its value represents the probability of success for the e-commerce platform in past web crawling attempts. They are respectively The preset weights.

[0010] Furthermore, the intelligent semantic analysis subsystem includes: The text preprocessing unit performs text preprocessing on the structured data of the e-commerce platform, including word segmentation, stop word filtering, synonym expansion, and special symbol cleaning. The basic semantic feature extraction unit extracts TF-IDF vectors, part-of-speech feature vectors, and dependency syntax vectors from the preprocessed text; and concatenates them to construct the basic semantic feature vector. The deep semantic modeling unit uses a pre-trained large language model that has been fine-tuned to perform deep semantic understanding on the pre-processed text and output deep semantic feature vectors. The semantic similarity retrieval unit maintains a semantic vector index library of historical violation cases, performs similar case retrieval on the deep semantic feature vectors output by the deep semantic modeling unit, and obtains a comprehensive historical prompt vector. The flexible violation joint discriminator combines the basic semantic feature vector, deep semantic feature vector, and comprehensive historical prompt vector to output candidate violation categories from the preprocessed text and classify them into strong violations, flexible violations, and compliance. For the candidate violation categories of flexible violations, context correction factors and historical consistency factors are combined to perform context correction, and a final decision is made.

[0011] Furthermore, the flexible violation joint discriminator adopts a three-layer progressive discriminant architecture, including: The first layer performs variant word and synonym detection on the preprocessed text, and outputs a set of candidate violation categories and coarse confidence scores. The second layer concatenates the basic semantic feature vector, deep semantic feature vector, and comprehensive historical prompt vector into a fusion feature, inputs it into the intent classifier, outputs candidate violation categories and intent probabilities, and classifies the candidate violation categories into strong violations, soft violations, and compliance according to preset thresholds. The third layer calculates a comprehensive score for candidate violation categories classified as flexible violations by combining the intent probability, contextual correction factor, and historical consistency factor of the candidate violation category, and outputs the final judgment result based on a preset threshold.

[0012] Preferably, the context correction factor is obtained through calculation. ;in: This represents the context correction factor, with a value range of [0,1]. The smaller the value, the stronger the limiting effect of offsetting illegal semantics in the text. This represents the intensity value of the k-th type of correction factor, which includes negative words, conditional qualifiers, and benevolent scenario markers. It is quantified based on the frequency of occurrence and semantic intensity level of the corresponding words; The preset weight of the k-th type of correction factor is either a fixed value preset by the system or obtained through sample training. The historical consistency factor is calculated. ;in: The historical consistency factor is represented, with a value range of [0, 1]; M represents the total number of historical records. This indicates the final violation category of the j-th historical judgment record; Indicates the current candidate violation category; This is an indicator function; it returns 1 if the violation categories are the same, and 0 otherwise. This is the current timestamp; The timestamp for the collection or identification of the j-th historical record; This is the attenuation coefficient, ranging from 0.1 to 0.5; the larger the value, the more pronounced the recent impact. As a time decay weight, the more recent the time, the greater the weight; Calculate the overall score. ; in The probability of intent for the current candidate violation category is calculated; the comprehensive score is compared with a preset threshold, and the final decision is made as a strong violation, a soft violation, or compliance.

[0013] Preferably, the semantic similarity retrieval unit uses an embedding model to map text into deep semantic vectors, and uses the HNSW algorithm to retrieve the top K historical cases with the highest similarity in the semantic vector index, outputting a list of similar cases and a comprehensive historical prompt vector; the list of similar cases = {(violation category, confidence level, similarity)1, (violation category, confidence level, similarity)2, ..., (violation category, confidence level, similarity)} K Then, the similar cases are aggregated by violation category, with weight = similarity × confidence, to obtain the score for each violation category. This score is then normalized into a C-dimensional probability vector, where C is the number of violation categories. This vector is the comprehensive historical warning vector.

[0014] Furthermore, it also includes an evidence archiving and traceability subsystem, which comprises: Raw data units are used for archiving and storing the complete HTML raw files and structured summaries of e-commerce platform pages; The semantic analysis result persistence unit is used to write the analysis results into an SQL data table; The evidence chain integrity verification unit uses a hash algorithm to generate and store data fingerprints of the archived storage content. The manual review interface unit pushes violation judgment results that are below the review threshold to the manual review queue.

[0015] Furthermore, the output of the manual review interface unit serves as a feedback sample, used to periodically and incrementally fine-tune the deep semantic modeling unit of the intelligent semantic analysis subsystem, thereby achieving continuous learning.

[0016] Compared with the prior art, the beneficial effects of the present invention include: 1. This invention achieves high-concurrency data collection across all e-commerce channels through a distributed crawler subsystem, which improves the speed by 3 to 5 times compared to a single-machine crawler.

[0017] 2. This invention achieves accurate identification of subtle violations, false advertising, malicious defamation, and other subtle violations through a multi-level semantic analysis architecture, with an accuracy rate more than 25 percentage points higher than that of traditional keyword matching methods.

[0018] 3. This invention achieves a complete closed loop from raw data collection to semantic analysis and violation identification through the evidence archiving and traceability subsystem, constructing a complete and credible evidence chain to ensure that the entire process is traceable and tamper-proof.

[0019] 4. Through knowledge transfer and continuous learning mechanisms involving manual review and feedback for fine-tuning, the system can automatically adapt to the dynamic evolution of illegal methods, significantly reducing the pressure on manual review. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the system architecture according to an embodiment of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0022] To make the objectives and features of the present invention more apparent and understandable, further explanation will be provided below in conjunction with the accompanying drawings and specific embodiments.

[0023] This embodiment proposes an e-commerce competition violation identification system based on distributed crawling and semantic analysis, such as... Figure 1 As shown, it includes: a distributed crawler subsystem, an intelligent semantic analysis subsystem, and an evidence archiving and traceability subsystem.

[0024] 1. Distributed web crawler subsystem: The distributed crawler subsystem constructs structured configuration information for each e-commerce platform, employs a Master-Worker architecture for crawler task scheduling, simulates normal access behavior to e-commerce platforms through request interval modulation and fingerprint randomization, provides different proxy strategies for different e-commerce platforms based on a dynamic proxy pool, and periodically collects structured data from the e-commerce platforms; specifically including: 1.1 Multi-platform compatible web crawler manager.

[0025] This is used to build and maintain structured configuration information for various e-commerce platforms, including platform domain names, coding standards, API interface addresses, request header templates, cookie management rules, etc., and predefines configurable crawling templates, which contain a list of URL patterns for product listing pages, detail pages, and review pages. The multi-platform adaptive crawler manager outputs e-commerce platform configuration objects for use by other units during execution scheduling and parsing.

[0026] 1.2 Distributed Task Scheduling Unit.

[0027] The distributed task scheduling unit adopts a Master-Worker architecture. The master node maintains the queue of tasks to be captured, the queue of tasks in progress, and the queue of tasks completed, and schedules tasks based on priority coefficients. Sort the tasks. ; in: As a time-sensitive factor, ; Indicates the current timestamp. Indicates the product's listing timestamp. This indicates the preset time window length; the timeliness factor reflects the freshness or urgency of updating the corresponding product information on the e-commerce platform. Products with more recent times (shorter from the current time) may have just experienced violations and need to be prioritized for capture.

[0028] Platform weight reflects the importance or priority of different e-commerce platforms in the violation monitoring task. The default weight for unimportant platforms is 0.4. For e-commerce platforms with a high incidence of violations or higher market supervision requirements, a higher weight is assigned.

[0029] This is the historical success rate factor; its value is the probability of success for a certain e-commerce platform (or similar e-commerce platforms) in past crawler crawling attempts.

[0030] They are respectively The preset weights.

[0031] Worker nodes periodically report load status, including CPU utilization, memory utilization, network bandwidth utilization, and local queue length. The master node can perform weighted round-robin scheduling and a hybrid scheduling of lowest load priority based on load scores.

[0032] 1.3 Dynamic Proxy Pool Unit.

[0033] Maintain a pool of IP proxies supporting HTTP, HTTPS, and SOCKS5 protocols, periodically verify proxy availability, remove invalid proxies, and replenish new resources. Employ different proxy strategies for different e-commerce platforms, such as changing the IP address for each request or maintaining session persistence.

[0034] 1.4 Structured data parsing unit.

[0035] Extract structured fields from the original HTML page, including product title, product details, and advertising copy. Examples include product title, product details, advertising copy, store name, price, image URL, publication time, sales volume, and rating. Fields that fail to parse are marked as empty. Output a structured record: {Record Identifier, Platform Name, Store Name, Product Title, Product Details, Advertising Copy, Original Page Storage Path, Collection Timestamp}.

[0036] 1.5 Incremental Acquisition Trigger.

[0037] The system periodically scans the e-commerce platform URLs in the completed crawling queue. If the difference between the current time and the last crawling time is greater than the preset re-crawl cycle for that e-commerce platform, the URL is added back to the crawling queue. The re-crawl cycle is configured based on the average update frequency of product information for each platform.

[0038] 2. Intelligent Semantic Analysis Subsystem: The intelligent semantic analysis subsystem receives structured data from the e-commerce platform collected by the distributed crawler subsystem, performs text preprocessing, basic semantic feature extraction, deep semantic feature extraction, and semantic similarity retrieval, and conducts flexible violation joint judgment. The flexible violation joint judgment uses context-corrected adjudication to progressively identify flexible violation behaviors. Specifically, this includes: 2.1 Text preprocessing unit.

[0039] The text preprocessing unit performs word segmentation, stop word filtering, synonym standardization, and special symbol cleaning on product titles, product details, and advertising slogans. The word segmentation can be based on bidirectional LSTM, which is a common practice and will not be elaborated further. Special symbol cleaning includes removing emoticons, redundant punctuation, etc.

[0040] The output preprocessed text is {word list, cleaned string, language type}.

[0041] 2.2 Basic semantic feature extraction unit.

[0042] Based on the word segmentation results, three basic features are extracted: TF-IDF vector (Term Frequency Inverse Document Frequency Vector): Based on the inverse document frequency of a corpus; Part-of-speech vector: Statistical analysis of the frequency of nouns, verbs, adjectives, degree adverbs, etc. Dependency syntactic vectors: A syntactic dependency tree is constructed using a dependency parser, with subject-verb, verb-object, attributive-head, and adverbial-head relations as edges and words as nodes, forming an adjacency matrix. The adjacency matrix is ​​then flattened to obtain the dependency syntactic vectors. The dependency parser is a classic text parsing tool in Natural Language Processing (NLP), specifically designed to analyze the syntactic dependencies between words within a sentence.

[0043] The output consists of a basic semantic feature vector composed of the above three parts.

[0044] 2.3 Deep Semantic Modeling Unit.

[0045] A pre-trained large language model is used, and supervised fine-tuning is performed using a labeled e-commerce product compliance dataset. The pre-processed input text is then used for inference, and deep semantic feature vectors are output.

[0046] For example, the pre-trained large language model can be based on the Qwen2-7B-Instruct pre-trained large model from Qianwen Open Source.

[0047] The e-commerce product compliance dataset consists of structured historical data, including product titles, details text, and advertising copy, collected by the distributed crawler subsystem, supplemented by publicly available violation cases from the platform and samples reviewed manually. Each sample is labeled with multiple tags, including violation categories such as false advertising, malicious defamation, and illegal promises. The initial size is no less than 50,000 records, and it will be continuously expanded through a manual review interface.

[0048] The supervised fine-tuning employs a parameter-efficient fine-tuning technique that freezes most of the model's parameters and updates only a small number of adaptation parameters to reduce memory usage and the risk of overfitting. For example, the LoRA (Low-Rank Adaptation) method can be used.

[0049] 2.4 Semantic similarity retrieval unit.

[0050] Maintain a semantic vector index (based on the HNSW algorithm) of labeled historical violation cases. For new text, use an embedding model to map the text into deep semantic vectors. Use the HNSW algorithm to retrieve the Top K most similar cases in the semantic vector index, returning a list of similar cases = {(violation category, confidence score, similarity)1, (violation category, confidence score, similarity)2, ..., (violation category, confidence score, similarity)}. K} and comprehensive historical prompt vector.

[0051] The comprehensive historical alert vector is weighted and aggregated according to the violation categories in the list of similar cases. The weight = similarity × confidence, and the score of each violation category is obtained. Then it is normalized into a C-dimensional probability vector, where C is the number of violation categories. This vector is the comprehensive historical alert vector.

[0052] 2.5 Flexible violation joint discrimination device.

[0053] A three-layer progressive discrimination architecture is adopted: Layer 1: Modified Word and Synonym Detection: By matching the sensitive word base table, modified word mapping table, and metaphor word mapping chain, modified word and synonym detection is performed on the preprocessed text. If at least one sensitive word, modified word, or metaphor word is matched, the candidate violation category set and coarse confidence score are output. If no word is matched, the process proceeds to Layer 2.

[0054] The method for calculating the coarse confidence score includes: Calculating the coarse confidence score: Assuming that M different sensitive words / variant words / metaphorical words are matched, and preset the weight of each word. For example, the keyword "number one in sales" has a weight of 0.9, and the keyword "competitors are speechless" has a weight of 0.7, etc. That is, the sum of the two numbers does not exceed 1.

[0055] Preset strong violation threshold If the coarse confidence level is greater than the strong violation threshold, the violation is directly output; otherwise, the second layer of discrimination is entered.

[0056] Second layer: Classifying violations using an intent classifier: The basic semantic feature vector, deep semantic feature vector, and comprehensive historical prompt vector of the preprocessed text are concatenated into a fusion feature, which is then input into the intent classifier.

[0057] The intent classifier employs a fully connected network + Sigmoid architecture. The fully connected layers map the concatenated high-dimensional fused features to a medium-dimensional latent space, extracting non-linear combined features. The GELU activation function introduces non-linearity to enhance model representation. Dropout prevents overfitting. The output includes K candidate violation categories and the intent probability vector for each category. Based on the preset threshold for severe violations. Weak violation threshold The candidate violation categories are divided into severe violations, mild violations, and compliance; among them, those with an intent probability greater than the severe violation threshold are considered. This constitutes a serious violation; the probability of intent is less than or equal to the serious violation threshold. Greater than the weak violation threshold This is considered a soft violation; the probability of intent is less than or equal to the weak violation threshold. To comply with regulations.

[0058] This layer determines each violation category separately based on probability values ​​and preset thresholds. Specifically, it assesses each category—such as "false advertising," "malicious defamation," and "illegal promises"—as a strong violation, a mild violation, or a compliant violation. Therefore, a single piece of text may be categorized into multiple levels of intensity (e.g., "false advertising" is a strong violation, while "malicious defamation" is a mild violation). The subsequent third-layer contextual correction adjudication process handles each category marked as a mild violation separately, ultimately outputting a list of violation categories and their respective intensity levels. These can also be aggregated into a single comprehensive violation level, which can be the highest intensity among all categories.

[0059] Third level: Contextual correction judgment: For candidate violation categories in the text that are identified as soft violations by the second layer, a comprehensive score is calculated by combining the intent probability, context correction factor, and historical consistency factor of the candidate violation category, and the final judgment result is output according to the preset threshold.

[0060] The context correction factor is calculated. ; in: This represents the context correction factor, with a value range of [0,1]. The smaller the value, the stronger the limiting effect of offsetting illegal semantics in the text. The intensity value of the k-th type of correction factor is given. The correction factors include negative words and conditional qualifiers. The correction factors involved in the text can be obtained by extracting negative word / qualifier markers from their basic semantic feature vectors. It is quantified based on the frequency of occurrence and semantic intensity level of the corresponding words; The preset weight of the k-th type of correction factor is either a fixed value preset by the system or obtained through sample training.

[0061] The historical consistency factor is calculated. ; in: This represents the historical consistency factor, with a value range of [0, 1]. M represents the total number of historical discrimination records; This indicates the final violation category of the j-th historical judgment record; Indicates the current candidate violation category; This is an indicator function; it returns 1 if the violation categories are the same, and 0 otherwise. This is the current timestamp; The timestamp for the collection or identification of the j-th historical record; This is the attenuation coefficient, ranging from 0.1 to 0.5; the larger the value, the more pronounced the recent impact. As a time decay weight, the more recent the time, the greater the weight.

[0062] Calculate the overall score. ; in The probability of intent for the current candidate violation category; the overall score is then compared with a preset strong violation threshold. Weak violation threshold The final decision is made based on the comparison results. The classification is either a serious violation, a minor violation, or compliance. A score exceeding the serious violation threshold is considered. The violation was ultimately classified as a serious violation; the overall score was less than or equal to the serious violation threshold. Greater than the weak violation threshold The violation was ultimately classified as a soft violation; the overall score was less than or equal to the weak violation threshold. The violation category was ultimately determined to be compliant.

[0063] If a text contains multiple violation categories with varying overall scores, the highest overall score is used as the final confidence level for that text.

[0064] 3. Evidence Archiving and Traceability Subsystem: This subsystem includes the following: 3.1 Raw Data Unit: Used for archiving and storing the complete HTML raw files and structured summaries of e-commerce platform pages. Specifically, the crawled raw HTML pages of e-commerce platforms are persistently stored, and key fields are extracted using RapidJSON to generate structured summaries and output storage indexes.

[0065] 3.2 Semantic Analysis Result Persistence Unit: Stores the cleaned text, basic feature vector, deep semantic vector, intent probability vector, final judgment, final confidence score, and similar case list into an SQL data table. Each record is associated with a record identifier of a structured record, and provides a query interface for historical judgment results and an update interface for the similar case index library.

[0066] 3.3 Evidence Chain Integrity Verification Unit: Calculates the SHA256 hash value for the entire archived storage content, generates a data fingerprint and stores it to verify that the data has not been tampered with.

[0067] 3.4 Manual Review Interface Unit: Preset Review Threshold The final confidence level will be lower than the preset review threshold. The text is pushed to the manual review queue, and the review results are used as a review sample to periodically readjust the deep semantic modeling unit and update the tables of sensitive words / variant words / metaphorical words.

[0068] This embodiment achieves high-concurrency data collection across all e-commerce channels through a distributed crawler subsystem. It enables accurate identification of subtle violations, such as false advertising, malicious defamation, and illegal promises. It establishes a complete closed loop from raw data collection to semantic analysis and violation identification, constructing a complete and credible chain of evidence. Through knowledge transfer and continuous learning mechanisms involving manual review and feedback for fine-tuning, the system can automatically adapt to the dynamic evolution of violation methods, significantly reducing the burden of manual review.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An e-commerce competition violation identification system based on distributed crawling and semantic analysis, characterized in that, It includes a distributed crawler subsystem and an intelligent semantic analysis subsystem; The distributed crawler subsystem constructs structured configuration information for each e-commerce platform, uses a Master-Worker architecture for crawler task scheduling, provides different proxy strategies for different e-commerce platforms based on a dynamic proxy pool, and periodically collects structured data from the e-commerce platforms. The intelligent semantic analysis subsystem receives structured data from the e-commerce platform collected by the distributed crawler subsystem, performs text preprocessing, basic semantic feature extraction, deep semantic feature extraction, and semantic similarity retrieval, and performs flexible violation joint judgment; the flexible violation joint judgment progressively judges flexible violation behavior through context-corrected adjudication. The intelligent semantic analysis subsystem includes: The text preprocessing unit performs text preprocessing on the structured data of the e-commerce platform, including word segmentation, stop word filtering, synonym expansion, and special symbol cleaning. The basic semantic feature extraction unit extracts TF-IDF vectors, part-of-speech feature vectors, and dependency syntax vectors from the preprocessed text; and concatenates them to construct the basic semantic feature vector. The deep semantic modeling unit uses a pre-trained large language model that has been fine-tuned to perform deep semantic understanding on the pre-processed text and output deep semantic feature vectors. The semantic similarity retrieval unit maintains a semantic vector index library of historical violation cases, performs similar case retrieval on the deep semantic feature vectors output by the deep semantic modeling unit, and obtains a comprehensive historical prompt vector. The flexible violation joint discriminator combines the basic semantic feature vector, deep semantic feature vector, and comprehensive historical prompt vector to output candidate violation categories from the preprocessed text and classify them into strong violations, flexible violations, and compliance. For candidate violation categories of flexible violations, contextual correction factors and historical consistency factors are combined for contextual correction, and a final decision is made. The context correction factor is calculated. ;in: This represents the context correction factor, with a value range of [0,1]. The smaller the value, the stronger the limiting effect of offsetting illegal semantics in the text. This represents the intensity value of the k-th type of correction factor, which includes negative words, conditional qualifiers, and benevolent scenario markers. It is quantified based on the frequency of occurrence and semantic intensity level of the corresponding words; The preset weight of the k-th type of correction factor is either a fixed value preset by the system or obtained through sample training. The historical consistency factor is calculated. ;in: The historical consistency factor is represented, with a value range of [0, 1]; M represents the total number of historical records. This indicates the final violation category of the j-th historical judgment record; Indicates the current candidate violation category; This is an indicator function; it returns 1 if the violation categories are the same, and 0 otherwise. This is the current timestamp; The timestamp for the collection or identification of the j-th historical record; This is the attenuation coefficient, ranging from 0.1 to 0.5; the larger the value, the more pronounced the recent impact. As a time decay weight, the more recent the time, the greater the weight; Calculate the overall score. ; in The probability of intent for the current candidate violation category is calculated; the comprehensive score is compared with a preset threshold, and the final decision is made as a strong violation, a soft violation, or compliance.

2. The e-commerce competition violation identification system based on distributed crawling and semantic analysis according to claim 1, characterized in that, The distributed crawler subsystem includes: A multi-platform compatible crawler manager that builds and maintains structured configuration information for various e-commerce platforms; The distributed task scheduling unit adopts a Master-Worker architecture for crawler task distribution and load balancing. The dynamic proxy pool unit maintains IP proxy resources and provides proxy distribution strategies for different e-commerce platforms. The structured data parsing unit extracts structured data, including product titles, details text, and advertising copy, from the acquired e-commerce platform pages; Incremental data collection triggers are configured to re-capture the data periodically based on the average update frequency of product information on each platform, thereby periodically updating the structured data of the e-commerce platform.

3. The e-commerce competition violation identification system based on distributed crawling and semantic analysis according to claim 2, characterized in that, The distributed task scheduling unit maintains a queue of tasks to be captured, a queue of tasks in progress, and a queue of tasks completed, and schedules tasks based on priority coefficients. Sort the tasks. ; in: As a time-sensitive factor, ; Indicates the current timestamp. Indicates the product's listing timestamp. Indicates the preset time window length; Platform weight reflects the importance or priority of different e-commerce platforms in the violation monitoring task; ; This is the historical success rate factor; its value represents the probability of success for the e-commerce platform in past web crawling attempts. They are respectively The preset weights.

4. The e-commerce competition violation identification system based on distributed crawling and semantic analysis according to claim 1, characterized in that, The flexible violation joint discriminator adopts a three-layer progressive discriminant architecture, including: The first layer performs variant word and synonym detection on the preprocessed text, and outputs a set of candidate violation categories and coarse confidence scores. The second layer concatenates the basic semantic feature vector, deep semantic feature vector, and comprehensive historical prompt vector into a fusion feature, inputs it into the intent classifier, outputs candidate violation categories and intent probabilities, and classifies the candidate violation categories into strong violations, soft violations, and compliance according to preset thresholds. The third layer calculates a comprehensive score for candidate violation categories classified as flexible violations by combining the intent probability, contextual correction factor, and historical consistency factor of the candidate violation category, and outputs the final judgment result based on a preset threshold.

5. The e-commerce competition violation identification system based on distributed crawling and semantic analysis according to claim 4, characterized in that, The semantic similarity retrieval unit uses an embedding model to map text into deep semantic vectors. It then uses the HNSW algorithm to retrieve the top K historical cases with the highest similarity from the semantic vector index, outputting a list of similar cases and a comprehensive historical suggestion vector. The similar case list = {(violation category, confidence level, similarity)1, (violation category, confidence level, similarity)2, ..., (violation category, confidence level, similarity)} K Then, the similar cases are aggregated by violation category, with weight = similarity × confidence, to obtain the score for each violation category. This score is then normalized into a C-dimensional probability vector, where C is the number of violation categories. This vector is the comprehensive historical warning vector.

6. The e-commerce competition violation identification system based on distributed crawling and semantic analysis according to claim 1, characterized in that, It also includes an evidence archiving and traceability subsystem, which comprises: Raw data units are used for archiving and storing the complete HTML raw files and structured summaries of e-commerce platform pages; The semantic analysis result persistence unit is used to write the analysis results into an SQL data table; The evidence chain integrity verification unit uses a hash algorithm to generate and store data fingerprints of the archived storage content. The manual review interface unit pushes violation judgment results that are below the review threshold to the manual review queue.

7. The e-commerce competition violation identification system based on distributed crawling and semantic analysis according to claim 6, characterized in that, The output of the manual review interface unit serves as a feedback sample, used to periodically and incrementally fine-tune the deep semantic modeling unit of the intelligent semantic analysis subsystem, thereby achieving continuous learning.

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