Real-time intelligent classification and tax fee estimation method and system applied to cross-border e-commerce imported commodities

Abstract

The invention discloses a real-time intelligent classification and tax fee estimation method and system applied to cross-border e-commerce imported commodities. The method comprises the following steps: S1, constructing and dynamically updating a simplified classification feature library; according to the method, a simplified classification feature library containing a'feature vector-HS Code-commodity frequency 'association structure is constructed, dynamic updating is realized, and a mutual information method is adopted to screen core features, so that the model complexity and deployment cost are reduced; through differentiated processing of a high-frequency commodity high-speed channel and a difficult commodity pruning decision-making tree algorithm, classification response is realized at a millisecond level, and classification accuracy is ensured at the same time; synchronous linkage of classification and tax calculation is realized based on a dynamic tax calculation rule base, and tax fee estimation delay is eliminated; through a structured log retention and accuracy verification optimization mechanism, user experience and compliance verification requirements are considered, and the efficiency, accuracy and stability of cross-border e-commerce import commodity classification and tax fee estimation are integrally improved.

Description

Technical Field

[0001] This invention relates to the field of e-commerce imported goods technology, and in particular to a real-time intelligent classification and tax estimation method and system for cross-border e-commerce imported goods. Background Technology

[0002] With the rapid development of global cross-border e-commerce, import orders under the B2C model are characterized by massive volume and high frequency. Consumers are increasingly demanding higher efficiency in order processing and transparency in taxation. Accurate classification of commodity tariff codes is a core link in cross-border e-commerce import business. It not only directly determines the standards for calculating tariffs, value-added tax and consumption tax, but also affects customs clearance efficiency and compliance risks. Existing cross-border e-commerce commodity classification technologies suffer from several shortcomings: 1. Traditional classification methods rely on manual review or simple keyword matching, resulting in low processing efficiency when faced with massive, high-frequency orders, failing to meet millisecond-level response requirements; 2. Existing intelligent classification models often employ complex commodity feature systems and general classification algorithms, leading to high model redundancy, high deployment costs, and difficulty in adapting to lightweight application scenarios; 3. The lack of a dedicated optimization mechanism for high-frequency commodities, with high-frequency and complex commodities using a unified processing channel, results in unreasonable resource allocation and further reduces overall processing efficiency; 4. The correlation between classification results and tax calculation rules is mostly processed asynchronously, leading to delays in tax estimation and impacting the consumer shopping experience. In light of these factors, there is an urgent need for a technical solution that can adapt to massive, high-frequency order scenarios, possesses a lightweight architecture, and achieves millisecond-level classification and real-time tax estimation to address the problems of low efficiency, poor adaptability, and unsatisfactory user experience in existing technologies. Therefore, this application proposes a real-time intelligent classification and tax estimation method and system for imported goods in cross-border e-commerce. Summary of the Invention

[0003] Based on the technical problems existing in the background technology, the present invention proposes a method and system for real-time intelligent classification and tax estimation of imported goods in cross-border e-commerce.

[0004] The real-time intelligent classification and tax estimation method for imported goods in cross-border e-commerce proposed in this invention includes the following steps: S1: Construct and dynamically update a simplified classification feature library: Select core classification features of products and establish a "feature vector-HS code-product frequency" association structure. By regularly updating and adapting to product iterations and policy changes, a data foundation is laid for subsequent rapid classification. S2: Access and preprocess order data: Clean and denoise the collected raw order data, extract core information, and generate standardized product feature data that matches the feature library to ensure data format uniformity and improve matching efficiency; S3: Based on product attributes, classify and output HS Code: By setting scientific thresholds, distinguish between high-frequency and difficult products, achieve differentiated processing, and balance classification speed and accuracy; S4: Synchronous invocation of tax calculation rules to estimate taxes and fees: Relying on a dynamically updated tax calculation rule library, various taxes and fees are calculated immediately after the HS Code is determined, breaking the traditional asynchronous processing mode; S5: Output results and retain logs: Synchronously feed back key information to relevant systems, while retaining processing traces, taking into account both user experience and compliance verification needs.

[0005] Preferably, the specific logical steps of S1 are as follows: S101: High-frequency commodity and basic classification data collection: Set a statistical period, collect commodity information and corresponding standard HS Code of cross-border e-commerce import orders within the period, filter high-frequency commodities according to the initial frequency threshold, and form sample dataset D; S102: Core Classification Feature Filtering: Extract candidate features for products, calculate the association strength between features and HS Code using mutual information, and retain core features that meet the association strength criteria. ;in This represents the mutual information value between feature f and HSCode. Represents the set of all possible values ​​for feature f. This represents the joint probability that feature f takes the value a and HS Code takes the value b; S103: Feature Standardization Process: Converting text features into numerical vectors. ;in Representation of features In the sample The number of times it appears in the sample set D, where n represents the total number of samples in the sample set D; The numerical features are then normalized using the following formula: ; in Represents the set of all possible values ​​for a given numerical feature class; S104: Association Structure Construction: Construct a three-dimensional association structure of "feature vector-HS code-product frequency", store it in an in-memory database and create an index to improve matching efficiency; S105: Dynamic update mechanism: Set the update cycle and use a sliding window method to update the product frequency. ; in The value represents the order frequency after product j is updated, T represents the end time of the current update cycle, and W represents the length of the sliding window.

[0006] Preferably, the specific logical steps of S2 are as follows: S201: Multi-source order data access and format alignment: Connecting to the open interfaces of mainstream cross-border e-commerce platforms' order data, collecting raw order data in real time, defining a unified data format template, and transforming heterogeneous data from different platforms into a structured data format to form the raw order dataset. ,in For a single order record, m is the total number of orders, and O is the original order dataset; S202: Data Cleaning and Denoising: Filter records with order statuses of "Cancelled" or "Refund", retain valid orders with statuses of "Pending Payment" or "Paid". For records with missing core fields, use attribute filling to fill in the missing values. For missing numerical values, fill in the missing values ​​using the average of the fields. The formula is: Missing values ​​in text fields were marked as "unknown" and subsequently manually reviewed. Outliers in numerical fields were identified and removed using the 3σ principle, with the following formula: ; in Here, k represents the mean of a numeric field, and k is the number of valid data entries for that field. For the i-th valid value of the field, The standard deviation of the numeric field; S203: Core Information Extraction: Based on the core feature library built in S1, the core information is extracted from the cleaned order data, and regular expressions are used to match key information. S204: Feature Standardization: Following the standardization rules of S1, the extracted core information is transformed into a standardized feature vector, ensuring consistency with the feature format of the simplified classification feature library, ultimately generating a standardized feature vector for each order item. Where k is the number of core features, This represents the standardized feature vector of the ordered goods. S205: Data Validation and Output: Validate whether the dimensions and format of the standardized feature vectors fully match the feature library, and remove any mismatched abnormal records; output the validated standardized data to the sorting and classification module for subsequent HSCode pre-classification.

[0007] Preferably, the specific logical steps of S3 are as follows: S301: Product Feature Matching Degree Calculation: Standardize the order product feature vector output from S2. The similarity is compared with the feature vectors in the simplified classification feature library, and the matching degree is calculated using cosine similarity: ;in Let i be the i-th dimension value of the order product feature vector. Let be the i-th dimension value of a product feature vector in the feature library, and k be the number of core features; S302: Product and Problematic Product Judgment: Setting Two Thresholds - High-Frequency Product Frequency Threshold and feature matching threshold The product type is determined by the following rules: ,in This refers to the historical order frequency of the current order's product in the feature library; S303: High-frequency commodity high-speed channel classification: If it is determined to be a high-frequency commodity, the mapping relationship of "feature vector-HS code" in the feature library is directly called to output the corresponding standard HS code. The time taken by this process is controlled in milliseconds. S304: Precise Analysis and Classification of Difficult-to-Classify Products: If a product is determined to be difficult to classify, a pruning-optimized decision tree algorithm is used for classification: A basic decision tree model is trained based on difficult-to-classify product samples in the feature library, and a pre-pruning strategy is used to control the tree depth and calculate the node purity gain. The formula used is as follows: ;in For the purity gain of decision tree nodes, Gini impurity of a decision tree node; Eliminate those with gain below the threshold The branch is then used to input the standardized feature vectors of the order items into the optimized decision tree model, and the HS Code pre-classification result is output. S305: Classification Result Verification: Perform basic compliance verification on the output HS Code. If the verification fails, mark the product as "manually reviewed" and trigger an alert.

[0008] Preferably, the specific logical steps of S4 are as follows: S401: Tax Rule Base Matching: Based on the HS Code output from S3, match the corresponding tax parameters for the goods from the dynamically updated tax rule base, including the tariff rate of the target importing country. Value Added Tax Consumption tax rate and the rules governing the basis of taxation; S402: Calculation of Dutiable Value: The dutiable value of imported goods through cross-border e-commerce is the transaction price of the goods. ,in Duty-paid price, The transaction price of the goods in the order; S403: Customs duties are calculated based on the dutiable value and the customs duty rate. The formula used is as follows: ; S404: Consumption Tax Calculation: For goods subject to consumption tax, the "composite tax price" is used for calculation, and the formula is as follows: ; ; in To form the taxable price, This is the amount of consumption tax; S405: Value Added Tax (VAT) Calculation: VAT is calculated based on "dutiable value + customs duty + consumption tax", formula: ; S406: Total Tax Summary and Output: Calculate the total tax on goods. The system simultaneously outputs customs duties, consumption tax, value-added tax, and total tax amounts to the e-commerce platform and order management system, while also recording the tax calculation basis. This refers to the total amount of taxes and fees levied on the goods. S407: Dynamic update and verification of tax calculation rules: Real-time synchronization of tax rate adjustment information from the customs of the target importing country. If the tax rate of the corresponding HS Code in the tax calculation rule database changes, automatic recalculation is triggered, and the output tax results are updated.

[0009] Preferably, the specific logical steps of S5 are as follows: S501: Output Information Integration: Integrate the HS Code pre-classification results from S3 with the tax estimation results from S4 to form a standardized output dataset. Single output record Includes the core field: Order Number Product ID HS Code, Customs Duties Consumption tax ,VAT Total taxes and fees Tax base and classification time Tax calculation period ; S502: Multi-system synchronous feedback: The HS Code, various tax amounts, total tax and tax base are pushed to the order details page of the e-commerce platform for consumers to view in real time. The complete output data is also pushed to the order management system and warehousing and logistics system to support subsequent customs declaration and logistics scheduling. In addition, in accordance with regulatory requirements, the core order information is synchronized to the customs supervision auxiliary system to ensure data traceability. S503: Handling Structured Log Retention: Building structured log templates for each log entry. Unique identifier for associated orders It retains the entire process trace and adopts a dual storage strategy of "local + cloud" for retention; S504: Log Indexing and Retrieval Optimization: Create multi-dimensional indexes for logs to support fast retrieval based on conditions; define the retrieval hit rate metric H: Regularly calculate H; if H < 99, optimize the index structure to improve retrieval efficiency. S505: Anomaly Log Warning Handling: Set anomaly judgment rules: If the "processing status" in the log is "manual review", trigger a system warning; generate anomaly log reports, including anomaly type, quantity, and percentage, and push the reports to the operation and maintenance team to support problem investigation and system optimization.

[0010] Preferably, after classification and tax calculation, the accuracy needs to be verified and optimized. Specifically, this involves periodically sampling a preset proportion of classification results and comparing them with manual review results to calculate the classification accuracy rate; simultaneously sampling tax estimation results and comparing them with the actual taxes and fees levied by customs to calculate the tax estimation error rate, using the following formula: Where E is the error rate for estimating taxes and fees for a single order. To estimate the total tax and fees for the system, If the classification accuracy rate is lower than the preset threshold or the average tax and fee estimation error rate is higher than the preset threshold, the feature weight adjustment of the core feature library in S1 and the parameter optimization of the decision tree algorithm in S3 will be triggered. At the same time, the tax calculation parameter calibration factor of the tax calculation rule library in S4 will be updated.

[0011] This invention also proposes a real-time intelligent classification and tax estimation system for imported goods in cross-border e-commerce, including a feature library construction module, an order access preprocessing module, a diversion and classification module, a tax calculation association estimation module, and a result output module; The feature library construction module is responsible for building a simplified classification feature library with a "feature vector-HS code-product frequency" association structure, and for dynamically updating and optimizing it; The order access preprocessing module is used to access multi-source order data and complete format alignment, cleaning and noise removal, core information extraction and standardization, and output feature vectors that are adapted to the feature library. The diversion and classification module distinguishes between high-frequency and difficult-to-identify products through matching degree calculation and threshold determination, realizes differentiated classification and outputs HS Code, and simultaneously completes classification verification; The tax calculation module matches tax calculation rules based on HS Code, calculates customs duties, value-added tax and consumption tax and summarizes total taxes and fees, and supports dynamic updates of tax calculation rules. The result output module integrates and categorizes tax results and synchronizes them to multiple systems, retains structured logs and optimizes log management, and triggers anomaly warnings and report pushes.

[0012] Compared with existing technologies, the beneficial effects of this invention are: By constructing a simplified classification feature library and a high-speed channel for high-frequency products, millisecond-level HS Code classification response is achieved, which greatly improves the processing efficiency of massive high-frequency orders and effectively solves the problem of low efficiency caused by traditional methods that rely on manual or simple matching. By using mutual information to filter core features, a lightweight feature library is built, reducing model deployment and maintenance costs, adapting to lightweight application scenarios, and effectively solving the problems of complex, redundant, and costly feature systems in existing intelligent models. By using threshold judgment to achieve differentiated classification, high-frequency products are directly matched and classified, while difficult products are accurately classified through optimized algorithms. This balances efficiency and accuracy while optimizing resource allocation, effectively solving the problems of unified processing of high-frequency and difficult products and unreasonable resource allocation. By immediately linking with the dynamic tax calculation rule base after the HS Code is determined to synchronously estimate taxes, the estimation delay is eliminated, the consumer shopping experience and tax transparency are improved, and the problems of asynchronous processing of classification and tax calculation and tax estimation delay in existing technologies are solved. By continuously optimizing classification accuracy and tax estimation accuracy, compliance risks are reduced, while adapting to product iterations and policy changes, the stability and applicability of the solution are improved. This invention constructs a simplified classification feature library with a "feature vector-HS code-product frequency" association structure and dynamically updates it. It employs mutual information to filter core features, reducing model complexity and deployment costs. Through differentiated processing using a high-speed channel for high-frequency products and a pruning decision tree algorithm for complex products, it achieves classification response in milliseconds while ensuring classification accuracy. It leverages a dynamic tax calculation rule library to achieve synchronous linkage between classification and tax calculation, eliminating tax estimation delays. Through structured log retention and accuracy verification optimization mechanisms, it balances user experience and compliance verification needs, comprehensively improving the efficiency, accuracy, and stability of cross-border e-commerce imported product classification and tax estimation, effectively solving problems such as low efficiency, poor adaptability, and unsatisfactory user experience in existing technologies. Attached Figure Description

[0013] Figure 1 This is a flowchart of the real-time intelligent classification and tax estimation method for imported goods in cross-border e-commerce proposed in this invention. Figure 2 This is a block diagram of the real-time intelligent classification and tax estimation system for imported goods in cross-border e-commerce proposed in this invention. Detailed Implementation

[0014] The present invention will be further explained below with reference to specific embodiments. Example

[0015] Reference Figure 1-2This embodiment proposes a real-time intelligent classification and tax estimation method for imported goods in cross-border e-commerce, including the following steps: S1: Construct and dynamically update a simplified classification feature library: Select core classification features of products and establish a "feature vector-HS code-product frequency" association structure. By regularly updating and adapting to product iterations and policy changes, a data foundation is laid for subsequent rapid classification. The specific logical steps are as follows: S101: High-frequency commodity and basic classification data collection: Set a statistical period, collect commodity information and corresponding standard HS Code of cross-border e-commerce import orders within the period, filter high-frequency commodities according to the initial frequency threshold, and form sample dataset D; S102: Core Classification Feature Filtering: Extract candidate features for products, calculate the association strength between features and HS Code using mutual information, and retain core features that meet the association strength criteria. ;in This represents the mutual information value between feature f and HS Code. Represents the set of all possible values ​​for feature f. This represents the joint probability that feature f takes the value a and HSCode takes the value b; S103: Feature Standardization Process: Converting text features into numerical vectors. ; in Representation of features In the sample The number of times it appears in the sample set D, where n represents the total number of samples in the sample set D; The numerical features are then normalized using the following formula: ; in Represents the set of all possible values ​​for a given numerical feature class; S104: Association Structure Construction: Construct a three-dimensional association structure of "feature vector-HS code-product frequency", store it in an in-memory database and create an index to improve matching efficiency; S105: Dynamic update mechanism: Set the update cycle and use a sliding window method to update the product frequency. ; in The value represents the order frequency after product j is updated, T represents the end time of the current update cycle, and W represents the length of the sliding window. S2: Access and preprocess order data: Clean and denoise the collected raw order data, extract core information, and generate standardized product feature data that matches the feature library to ensure data format uniformity and improve matching efficiency; The specific logical steps are as follows: S201: Multi-source order data access and format alignment: Connecting to the open interfaces of mainstream cross-border e-commerce platforms' order data, collecting raw order data in real time, defining a unified data format template, and transforming heterogeneous data from different platforms into a structured data format to form the raw order dataset. ,in For a single order record, m is the total number of orders, and O is the original order dataset; S202: Data Cleaning and Denoising: Filter records with order statuses of "Cancelled" or "Refund", retain valid orders with statuses of "Pending Payment" or "Paid". For records with missing core fields, use attribute filling to fill in the missing values. For missing numerical values, fill in the missing values ​​using the average of the fields. The formula is: Missing values ​​in text fields were marked as "unknown" and subsequently manually reviewed. Outliers in numerical fields were identified and removed using the 3σ principle, with the following formula: ; in Here, k represents the mean of a numeric field, and k is the number of valid data entries for that field. For the i-th valid value of the field, The standard deviation of the numeric field; S203: Core Information Extraction: Based on the core feature library built in S1, the core information is extracted from the cleaned order data, and regular expressions are used to match key information. S204: Feature Standardization: Following the standardization rules of S1, the extracted core information is transformed into a standardized feature vector, ensuring consistency with the feature format of the simplified classification feature library, ultimately generating a standardized feature vector for each order item. Where k is the number of core features, This represents the standardized feature vector of the ordered goods. S205: Data Validation and Output: Validate whether the dimensions and format of the standardized feature vectors completely match the feature library, and remove any mismatched abnormal records; output the validated standardized data to the sorting and classification module for subsequent HSCode pre-classification. S3: Based on product attributes, classify and output HS Code: By setting scientific thresholds, distinguish between high-frequency and difficult products, achieve differentiated processing, and balance classification speed and accuracy; The specific logical steps are as follows: S301: Product Feature Matching Degree Calculation: Standardize the order product feature vector output from S2. The similarity is compared with the feature vectors in the simplified classification feature library, and the matching degree is calculated using cosine similarity: ; in Let i be the i-th dimension value of the order product feature vector. Let be the i-th dimension value of a product feature vector in the feature library, and k be the number of core features; S302: Product and Problematic Product Judgment: Setting Two Thresholds - High-Frequency Product Frequency Threshold and feature matching threshold The product type is determined by the following rules: ,in This refers to the historical order frequency of the current order's product in the feature library; S303: High-frequency commodity high-speed channel classification: If it is determined to be a high-frequency commodity, the mapping relationship of "feature vector-HS code" in the feature library is directly called to output the corresponding standard HS code. The time taken by this process is controlled in milliseconds. S304: Precise Analysis and Classification of Difficult-to-Classify Products: If a product is determined to be difficult to classify, a pruning-optimized decision tree algorithm is used for classification: A basic decision tree model is trained based on difficult-to-classify product samples in the feature library, and a pre-pruning strategy is used to control the tree depth and calculate the node purity gain. The formula used is as follows: ;in For the purity gain of decision tree nodes, Gini impurity of a decision tree node; Eliminate those with gain below the threshold The branch is then used to input the standardized feature vectors of the order items into the optimized decision tree model, and the HS Code pre-classification result is output. S305: Classification result verification: Perform basic compliance verification on the output HS Code. If the verification fails, mark the product as "manually reviewed" and trigger an alert. S4: Synchronous invocation of tax calculation rules to estimate taxes and fees: Relying on a dynamically updated tax calculation rule library, various taxes and fees are calculated immediately after the HS Code is determined, breaking the traditional asynchronous processing mode; The specific logical steps are as follows: S401: Tax Rule Base Matching: Based on the HS Code output from S3, match the corresponding tax parameters for the goods from the dynamically updated tax rule base, including the tariff rate of the target importing country. Value Added Tax Consumption tax rate and the rules governing the basis of taxation; S402: Calculation of Dutiable Value: The dutiable value of imported goods through cross-border e-commerce is the transaction price of the goods. ,in Duty-paid price, The transaction price of the goods in the order; S403: Customs duties are calculated based on the dutiable value and the customs duty rate. The formula used is as follows: ; S404: Consumption Tax Calculation: For goods subject to consumption tax, the "composite tax price" is used for calculation, and the formula is as follows: ; ; in To form the taxable price, This is the amount of consumption tax; S405: Value Added Tax (VAT) Calculation: VAT is calculated based on "dutiable value + customs duty + consumption tax", formula: ; S406: Total Tax Summary and Output: Calculate the total tax on goods. The system simultaneously outputs customs duties, consumption tax, value-added tax, and total tax amounts to the e-commerce platform and order management system, while also recording the tax calculation basis. This refers to the total amount of taxes and fees levied on the goods. S407: Dynamic update and verification of tax calculation rules: Real-time synchronization of tax rate adjustment information from the customs of the target importing country. If the tax rate of the corresponding HS Code in the tax calculation rule database changes, automatic recalculation is triggered, and the output tax results are updated. In addition, after classification and tax calculation, the accuracy needs to be verified and optimized. Specifically, this involves periodically sampling a preset proportion of classification results and comparing them with manual review results to calculate the classification accuracy rate; simultaneously sampling tax estimation results and comparing them with the actual taxes and fees levied by customs to calculate the tax estimation error rate. The formula used is as follows: Where E is the error rate for estimating taxes and fees for a single order. To estimate the total tax and fees for the system, If the classification accuracy rate is lower than the preset threshold or the average tax and fee estimation error rate is higher than the preset threshold, the feature weight adjustment of the core feature library in S1 and the parameter optimization of the decision tree algorithm in S3 will be triggered, and the tax calculation parameter calibration factor of the tax calculation rule library in S4 will be updated. S5: Output results and retain logs: Synchronously feed back key information to relevant systems, while retaining processing traces, taking into account both user experience and compliance verification needs; The specific logical steps are as follows: S501: Output Information Integration: Integrate the HS Code pre-classification results from S3 with the tax estimation results from S4 to form a standardized output dataset. Single output record Includes the core field: Order Number Product ID HS Code, Customs Duties Consumption tax ,VAT Total taxes and fees Tax base and classification time Tax calculation period ; S502: Multi-system synchronous feedback: The HS Code, various tax amounts, total tax and tax base are pushed to the order details page of the e-commerce platform for consumers to view in real time. The complete output data is also pushed to the order management system and warehousing and logistics system to support subsequent customs declaration and logistics scheduling. In addition, in accordance with regulatory requirements, the core order information is synchronized to the customs supervision auxiliary system to ensure data traceability. S503: Handling Structured Log Retention: Building structured log templates for each log entry. Unique identifier for associated orders It retains the entire process trace and adopts a dual storage strategy of "local + cloud" for retention; S504: Log Indexing and Retrieval Optimization: Create multi-dimensional indexes for logs to support fast retrieval based on conditions; define the retrieval hit rate metric H: Regularly calculate H; if H < 99, optimize the index structure to improve retrieval efficiency. S505: Anomaly Log Warning Handling: Set anomaly judgment rules: If the "processing status" in the log is "manual review", trigger a system warning; generate anomaly log reports, including anomaly type, quantity, and percentage, and push the reports to the operation and maintenance team to support problem investigation and system optimization.

[0016] This embodiment also proposes a real-time intelligent classification and tax estimation system for imported goods in cross-border e-commerce, including a feature library construction module, an order access preprocessing module, a diversion and classification module, a tax calculation association estimation module, and a result output module. The feature library construction module is responsible for building a simplified classification feature library with a "feature vector-HS code-product frequency" association structure, and for dynamically updating and optimizing it; The order access preprocessing module is used to access multi-source order data and complete format alignment, cleaning and noise removal, core information extraction and standardization, and output feature vectors that are adapted to the feature library. The sorting and classification module distinguishes between high-frequency and difficult-to-identify products by matching degree calculation and threshold judgment, realizes differentiated classification and outputs HS Code, and simultaneously completes classification verification; The tax calculation module matches tax rules based on HS Code, calculates customs duties, value-added tax and consumption tax and summarizes the total tax and fees, and supports dynamic updates of tax rules; The results output module integrates and categorizes tax results and synchronizes them to multiple systems, retains structured logs and optimizes log management, and triggers anomaly alerts and report pushes. This embodiment constructs a simplified classification feature library with a "feature vector-HS code-product frequency" association structure and dynamically updates it. It uses mutual information to filter core features, reducing model complexity and deployment costs. Through differentiated processing of high-frequency products via a high-speed channel and difficult-to-identify products via a pruning decision tree algorithm, it achieves classification response in milliseconds while ensuring classification accuracy. It relies on a dynamic tax calculation rule library to achieve synchronous linkage between classification and tax calculation, eliminating tax estimation delays. Through structured log retention and accuracy verification optimization mechanisms, it balances user experience and compliance verification needs, comprehensively improving the efficiency, accuracy, and stability of cross-border e-commerce imported product classification and tax estimation, effectively solving problems such as low efficiency, poor adaptability, and unsatisfactory user experience in existing technologies.

[0017] The above are merely preferred embodiments 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 real-time intelligent classification and tax estimation method applied to cross-border e-commerce imported goods, characterized in that, Includes the following steps: S1: Construct and dynamically update a simplified classification feature library: Filter core classification features of products and establish a "feature vector-HSCode-product frequency" association structure. By regularly updating and adapting to product iterations and policy changes, a data foundation is laid for subsequent rapid classification. S2: Access and preprocess order data: Clean and denoise the collected raw order data, extract core information, and generate standardized product feature data that matches the feature library to ensure data format uniformity and improve matching efficiency; S3: Based on product attributes, classify and output HS Code: By setting scientific thresholds, distinguish between high-frequency and difficult products, achieve differentiated processing, and balance classification speed and accuracy; S4: Synchronous invocation of tax calculation rules to estimate taxes and fees: Relying on a dynamically updated tax calculation rule library, various taxes and fees are calculated immediately after the HS Code is determined, breaking the traditional asynchronous processing mode; S5: Output results and retain logs: Synchronously feed back key information to relevant systems, while retaining processing traces, taking into account both user experience and compliance verification needs. 2.The method of real-time intelligent classification and duty estimation for cross-border e-commerce imported goods according to claim 1, wherein, The specific logical steps of S1 are as follows: S101: High-frequency commodity and basic classification data collection: Set a statistical period, collect commodity information and corresponding standard HS Code of cross-border e-commerce import orders within the period, filter high-frequency commodities according to the initial frequency threshold, and form sample dataset D; S102: Core Classification Feature Filtering: Extract candidate features for products, calculate the association strength between features and HS Code using mutual information, and retain core features that meet the association strength criteria. ； wherein denotes the mutual information value of feature f with HS Code, denotes the set of all possible values of feature f, denotes the joint probability of feature f taking value a and HS Code taking value b; S103: Feature Standardization Process: Converting text features into numerical vectors. ； wherein representing features in the sample number of occurrences in the sample, n represents the total number of samples of the sample set D; And the numerical class feature is normalized, and the formula used is: ; wherein all value sets of a certain numerical value characteristic; S104: Association Structure Construction: Construct a three-dimensional association structure of "feature vector-HS code-product frequency", store it in an in-memory database and create an index to improve matching efficiency; S105: Dynamic updating mechanism: set the updating period, and update the commodity frequency by using the sliding window method: ; wherein denotes the updated order frequency of item j, T denotes the end time of the current update period, and W denotes the length of the sliding window. 3.The method of real-time intelligent classification and duty estimation for cross-border e-commerce imported goods according to claim 1, wherein, The specific logical steps of S2 are as follows: S201: Multi-source order data access and format alignment: Connect the order open interface of mainstream cross-border e-commerce platforms, collect raw order data in real time, define a unified data format template, convert heterogeneous data from different platforms into structured data format, and form a raw order data set wherein is a single order record, m is the total number of orders, and O is the raw order data set. S202: Data cleaning and denoising: filter the records with order status "cancel" and "refund", keep the valid orders with order status "to be paid" and "paid", for the records with missing core fields, use attribute filling method to supplement, fill the missing values of numerical type with field mean, the formula is: , the missing values of text type are marked as "unknown" and manually reviewed later, and the outliers of numerical type fields are identified and removed by 3σ principle, and the formula used is: ； wherein is the mean value of the numerical field, k is the number of valid data of a certain field, is the i-th valid value of the field, is the standard deviation of the numerical field; S203: Core Information Extraction: Based on the core feature library built in S1, the core information is extracted from the cleaned order data, and regular expressions are used to match key information. S204: Feature standardization: follow the standardization rules of S1 to convert the extracted core information into a standardized feature vector, ensure that the feature format is consistent with the simplified classification feature library, and finally generate a standardized feature vector for each order commodity: where k is the number of core features, is the standardized feature vector of the order commodity; S205: Data Validation and Output: Validate whether the dimensions and format of the standardized feature vectors fully match the feature library, and remove any mismatched abnormal records; output the validated standardized data to the sorting and classification module for subsequent HS Code pre-classification. 4.The method of real-time intelligent categorization and duty estimation for cross-border e-commerce imported goods according to claim 1, wherein, The specific logical steps of S3 are as follows: S301: Commodity feature matching degree calculation: the order commodity standardized feature vector output by S2 is matched with the simplified classification feature library feature vector in similarity, and the cosine similarity is used to calculate the matching degree: , ； in Let i be the i-th dimension value of the order product feature vector. Let be the i-th dimension value of a product feature vector in the feature library, and k be the number of core features; S302: Product and Problematic Product Judgment: Setting Two Thresholds - High-Frequency Product Frequency Threshold and feature matching threshold The product type is determined by the following rules: ,in This refers to the historical order frequency of the current order's product in the feature library; S303: High-frequency product high-speed channel classification: If it is determined to be a high-frequency product, the mapping relationship of "feature vector-HSCode" in the feature library is directly called to output the corresponding standard HS Code. The time taken by this process is controlled in milliseconds. S304: Precise Analysis and Classification of Difficult-to-Classify Products: If a product is determined to be difficult to classify, a pruning-optimized decision tree algorithm is used for classification: A basic decision tree model is trained based on difficult-to-classify product samples in the feature library, and a pre-pruning strategy is used to control the tree depth and calculate the node purity gain. The formula used is as follows: ;in For the purity gain of decision tree nodes, Gini impurity of a decision tree node; Eliminate those with gain below the threshold The branch is then used to input the standardized feature vectors of the order items into the optimized decision tree model, and the HS Code pre-classification result is output. S305: Classification Result Verification: Perform basic compliance verification on the output HS Code. If the verification fails, mark the product as "manually reviewed" and trigger an alert.

5. The real-time intelligent classification and tax estimation method for imported goods in cross-border e-commerce according to claim 1, characterized in that, The specific logical steps of S4 are as follows: S401: Tax Rule Base Matching: Based on the HS Code output from S3, match the corresponding tax parameters for the goods from the dynamically updated tax rule base, including the tariff rate of the target importing country. Value Added Tax Consumption tax rate and the rules governing the basis of taxation; S402: Calculation of Dutiable Value: The dutiable value of imported goods through cross-border e-commerce is the transaction price of the goods. ,in Duty-paid price, The transaction price of the goods in the order; S403: Customs duties are calculated based on the dutiable value and the customs duty rate. The formula used is as follows: ; S404: Consumption Tax Calculation: For goods subject to consumption tax, the "composite tax price" is used for calculation, and the formula is as follows: ； ； in To form the taxable price, This is the amount of consumption tax; S405: Value Added Tax (VAT) Calculation: VAT is calculated based on "dutiable value + customs duty + consumption tax". The formula is as follows: ; S406: Total Tax Summary and Output: Calculate the total tax on goods. The system simultaneously outputs customs duties, consumption tax, value-added tax, and total tax amounts to the e-commerce platform and order management system, while also recording the tax calculation basis. This refers to the total amount of taxes and fees levied on the goods. S407: Dynamic update and verification of tax calculation rules: Real-time synchronization of tax rate adjustment information from the customs of the target importing country. If the tax rate of the corresponding HS Code in the tax calculation rule database changes, automatic recalculation is triggered, and the output tax results are updated.

6. The real-time intelligent classification and tax estimation method for imported goods in cross-border e-commerce according to claim 1, characterized in that, The specific logical steps of S5 are as follows: S501: Output Information Integration: Integrate the HS Code pre-classification results from S3 with the tax estimation results from S4 to form a standardized output dataset. Single output record Includes the core field: Order Number Product ID HS Code, Customs Duty Consumption tax ,VAT Total taxes and fees Tax base and classification time Tax calculation period ; S502: Multi-system synchronous feedback: The HS Code, various tax amounts, total tax and tax base are pushed to the order details page of the e-commerce platform for consumers to view in real time. The complete output data is also pushed to the order management system and warehousing and logistics system to support subsequent customs declaration and logistics scheduling. In addition, in accordance with regulatory requirements, the core order information is synchronized to the customs supervision auxiliary system to ensure data traceability. S503: Handling Structured Log Retention: Building structured log templates for each log entry. Unique identifier for associated orders It retains the entire process trace and adopts a dual storage strategy of "local + cloud" for retention; S504: Log Indexing and Retrieval Optimization: Create multi-dimensional indexes for logs to support fast retrieval based on conditions; define the retrieval hit rate metric H: Regularly calculate H; if H < 99, optimize the index structure to improve retrieval efficiency. S505: Anomaly Log Warning Handling: Set anomaly judgment rules: If the "processing status" in the log is "manual review", trigger a system warning; Generate anomaly log reports, including anomaly type, quantity, and percentage, and push the reports to the operations and maintenance team to support problem investigation and system optimization.

7. The real-time intelligent classification and tax estimation method for imported goods in cross-border e-commerce according to claim 1, characterized in that, After classification and tax calculation, the accuracy needs to be verified and optimized. Specifically, this involves periodically sampling a preset proportion of classification results and comparing them with manual review results to calculate the classification accuracy rate; simultaneously, sampling tax estimation results and comparing them with the actual taxes and fees levied by customs to calculate the tax estimation error rate. The formula used is as follows: Where E is the error rate for estimating taxes and fees for a single order. To estimate the total tax and fees for the system, If the classification accuracy rate is lower than the preset threshold or the average tax and fee estimation error rate is higher than the preset threshold, the feature weight adjustment of the core feature library in S1 and the parameter optimization of the decision tree algorithm in S3 will be triggered. At the same time, the tax calculation parameter calibration factor of the tax calculation rule library in S4 will be updated.

8. A real-time intelligent classification and tax estimation system for imported goods in cross-border e-commerce, used to implement the method described in any one of claims 1-7, characterized in that, It includes a feature library construction module, an order access preprocessing module, a traffic classification module, a tax calculation and estimation module, and a result output module; The feature library construction module is responsible for building a simplified classification feature library with a "feature vector-HS code-product frequency" association structure, and for dynamically updating and optimizing it; The order access preprocessing module is used to access multi-source order data and complete format alignment, cleaning and noise removal, core information extraction and standardization, and output feature vectors that are adapted to the feature library. The diversion and classification module distinguishes between high-frequency and difficult-to-identify products through matching degree calculation and threshold determination, realizes differentiated classification and outputs HS Code, and simultaneously completes classification verification; The tax calculation module matches tax calculation rules based on HS Code, calculates customs duties, value-added tax and consumption tax and summarizes total taxes and fees, and supports dynamic updates of tax calculation rules. The result output module integrates and categorizes tax results and synchronizes them to multiple systems, retains structured logs and optimizes log management, and triggers anomaly warnings and report pushes.

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