SEO optimization method based on multi-language commodity description translation
By analyzing historical data from e-commerce platforms to identify layout conflict patterns in multilingual product descriptions, constructing semantic topology trees, and generating translated text that conforms to platform rules, the problem of layout adaptability in multilingual product information translation is solved, improving translation efficiency and display effects.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- YUEYANG LIYI TECH CO LTD
- Filing Date
- 2026-01-29
- Publication Date
- 2026-05-08
AI Technical Summary
Existing technologies lack sensitivity identification and control over the layout rules of target platforms during the translation of multilingual product information. This makes it difficult for translation results to simultaneously achieve semantic accuracy and layout adaptability, thus affecting the exposure and conversion rate of products on the target platform.
By mining historical multilingual product description data from e-commerce platforms, we can identify semantic conflict patterns that are sensitive to platform layout, construct a semantic topology tree, and generate multilingual product description text that meets platform layout requirements. This reduces the risk of content violations and improves the automation and efficiency of translation.
It enables proactive identification and avoidance of platform layout rules during the translation of multilingual product information, ensuring that the content meets platform requirements, improving the search ranking and display effect of product information, and reducing reliance on manual review.
Smart Images
Figure CN121997953A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, specifically to an SEO optimization method based on multilingual product description translation. Background Technology
[0002] As e-commerce becomes increasingly international, accurate translation and compliant publication of multilingual product information have become crucial for e-commerce platforms to enhance their market competitiveness. Currently, mainstream product information translation technologies typically focus on the conversion of the language content itself and semantic accuracy. However, insufficient consideration is often given to the target platform's layout rules and search engine optimization requirements during the translation process. This results in some translations, while linguistically accurate, conflicting with the target platform's marketing plans and content layout guidelines, thus hindering the effective display of product information.
[0003] Traditional methods for addressing these issues typically rely on manual review or subsequent compliance checks. This approach is not only labor-intensive and inefficient, but also fails to fundamentally eliminate layout compliance risks during product information publishing. Furthermore, existing technologies lack systematic identification and control mechanisms for platform-specific layout sensitivities during the translation content generation stage. This makes it difficult for translated content to simultaneously achieve semantic accuracy and layout suitability, thus impacting product exposure and conversion rates on the target platform. Summary of the Invention
[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an SEO optimization method based on multilingual product description translation.
[0005] To achieve the above objectives, this invention provides an SEO optimization method based on multilingual product description translation, comprising: Based on the historical multilingual product description data of the target e-commerce platform, we can mine semantic conflict patterns that are sensitive to platform layout, and generate an initial set of semantic vectors by combining the product descriptions in the source language to be translated. Based on the platform's preset layout template, the structural mapping relationship between the initial semantic vector set and the semantic conflict pattern is determined, and the semantic fragments in the initial semantic vector set that trigger the risk of platform layout conflict are identified. Based on the semantic fragments that trigger platform layout conflict risks, a semantic topology tree adapted to platform layout requirements is constructed by combining the initial semantic vector set, and semantic unit replacement combinations that can eliminate layout conflict risks are generated according to the semantic conflict pattern. Based on the semantic topology tree, semantic units are replaced and combined to reorganize the layout, generating multilingual product description text that conforms to the SEO layout rules of the target e-commerce platform.
[0006] Compared with the prior art, the beneficial effects of the present invention are: This invention introduces a layout-sensitive semantic conflict mining technology based on historical data to proactively identify and avoid layout risks in multilingual product descriptions. It ensures that the content complies with the platform's layout requirements from the translation generation stage, reducing the possibility of product display restrictions due to content violations.
[0007] This invention establishes a structural mapping relationship between semantic content and platform layout templates, enabling the translation process of product information to not only maintain semantic consistency between languages but also conform to the layout specifications of specific areas of e-commerce platforms, thereby improving the overall performance of multilingual product information in search ranking and display effects.
[0008] This invention constructs a structured propagation analysis mechanism for layout risks and provides corresponding semantic unit replacement schemes, enabling the product information optimization process to have a clear processing path and a high degree of automation, reducing reliance on manual review, and improving the efficiency and quality of product information release on cross-border e-commerce platforms in multilingual environments. Attached Figure Description
[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0010] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation
[0011] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0012] Please see Figure 1 This embodiment provides an SEO optimization method based on multilingual product description translation, including: S101: Based on the historical multilingual product description data of the target e-commerce platform, mine the semantic conflict patterns that are sensitive to the platform layout, and generate an initial set of semantic vectors by combining the product descriptions in the source language to be translated. Specifically, the mining platform is configured with semantically sensitive conflict patterns, including: Based on historical multilingual product description data, a cross-lingual semantic co-occurrence network is generated; It should be noted that the historical multilingual product description data in this step specifically refers to the collection of real product description data in different languages accumulated by e-commerce platforms (such as product titles, promotional phrases, etc.), preferably including historical corpora in multiple languages such as English, German, and Spanish.
[0013] In one specific embodiment, the historical product description corpus is first subjected to cross-lingual semantic embedding processing. Specifically, a cross-lingual semantic embedding model (preferably mBERT or XLM-R model) is used to convert each product description data into a vector representation in a unified semantic space. Each historical product description data... For example, generate its corresponding semantic vector: Wherein, Embedding represents a pre-trained cross-lingual semantic embedding model, and the semantic embedding vector can effectively represent the semantic features of multilingual product description text.
[0014] Furthermore, based on the co-occurrence of semantic units in historical corpora, a cross-linguistic semantic co-occurrence network is constructed. This semantic co-occurrence network is defined as a graph structure. The network node set V represents frequently occurring semantic units (such as promotional phrases like "free delivery" and "big discount"), the edge set E represents the co-occurrence relationship between semantic units, and the edge weight set W represents the frequency of two semantic units appearing simultaneously.
[0015] For example, historical data statistics show that the English phrases "free shipping" and "big discount" frequently co-occur, with a co-occurrence frequency of 0.75. Therefore, an edge connecting "free shipping" and "big discount" is constructed in the network, with an edge weight of 0.75.
[0016] Based on the topological stability change trend of co-occurring network nodes, identify layout-sensitive abnormal nodes; The determination of layout-sensitive abnormal nodes includes: Analyze the topology changes of co-occurring network nodes before and after historical layout violation events to identify nodes with topology changes; It should be understood that layout violation incidents refer to clearly recorded events that actually occur during platform operations, resulting in the removal of products or a drop in search ranking due to specific semantic combinations violating the platform's layout rules.
[0017] In a specific embodiment, co-occurrence network snapshots are constructed before and after the occurrence of historical layout violation events (e.g., one month before and after the event), and are defined as follows: and Furthermore, the topological changes of each node in these two network snapshots are calculated, preferably using node degree centrality. The formula for calculating the changes in the indicator is as follows: In the formula, and Representing nodes respectively Degree centrality values before and after a violation event. When the absolute value of the change in a node's topology metric exceeds a threshold (e.g., a threshold of 0.2), the node is... Defined as a topology change node.
[0018] Based on the correlation between the semantic features of topology change nodes and the platform's layout violation history, layout-sensitive abnormal nodes are identified. It should be noted that not all nodes with topology changes are directly related to violations. Therefore, it is necessary to further combine the semantic features of the nodes with the semantic features of historical violations to identify layout-sensitive abnormal nodes.
[0019] In practice, cosine similarity (Sim) is used to calculate the semantic similarity between the semantic embedding vector of the topology-changed node and the high-risk semantic fragments in historical layout violation events. The calculation formula is as follows: ;in, Represents topology change nodes semantic embedding vector, Indicates historical violation semantic fragments The semantic embedding vector. When the semantic similarity Sim is greater than a threshold (e.g., 0.85), the corresponding node is identified as a layout-sensitive abnormal node.
[0020] Based on the semantic conflict structural features of layout-sensitive abnormal nodes, clustering is used to form layout-sensitive semantic conflict patterns; It is understandable that a single layout-sensitive anomalous node cannot constitute a complete semantic conflict pattern. Therefore, further clustering analysis is performed on the above-mentioned anomalous nodes to form multiple sets of layout-sensitive semantic conflict patterns.
[0021] In practice, a clustering algorithm (preferably K-means or DBSCAN) is used to perform clustering analysis on the semantic embedding vectors of layout-sensitive abnormal nodes. The clustering process is defined as follows: Let the set of layout-sensitive abnormal nodes be... The clustering result is then defined as: In the formula, It is a set of multiple semantic conflict patterns after being processed by the clustering algorithm, and each cluster represents a layout-sensitive semantic conflict pattern.
[0022] For example, if the clustering result shows that the nodes "free shipping" and "big discount" are clustered into the same cluster, then this cluster is defined as representing a layout-sensitive semantic conflict pattern (i.e., the combination of "free shipping + big discount") for subsequent risk assessment.
[0023] Furthermore, an initial set of semantic vectors is generated by combining the product description in the source language to be translated; Specifically, to ensure the accuracy of subsequent semantic risk assessment, the product description in the source language to be translated is divided into semantic units. The preferred method for this division is phrase-level semantic unit division based on dependency parsing. In one specific embodiment, the product description text in the source language to be translated (e.g., English product description) is syntactically analyzed and broken down into multiple sets of semantically complete units: In the formula, These are the semantic units after being broken down, such as independent phrases like "free shipping" and "big discount".
[0024] Then, each semantic unit is processed using a cross-language semantic embedding model to generate its own semantic embedding vector: ; Preferably, the embedding uses the same cross-lingual embedding model (such as mBERT or XLM-R) as the historical data mentioned above, to ensure that the description to be translated is in the same semantic space as the historical data, which facilitates subsequent risk assessment.
[0025] Furthermore, based on the original text order of each semantic unit, an initial set of semantic vectors containing sequential structure is formed: Each element contains a semantic unit text and its corresponding vector, which can simultaneously reflect semantic information and structural order relationship to support subsequent analysis.
[0026] S102: Based on the platform's preset layout template, determine the structural mapping relationship between the initial semantic vector set and the semantic conflict pattern, and identify the semantic fragments in the initial semantic vector set that trigger the risk of platform layout conflict. Specifically, the identification of semantic segments in the initial semantic vector set that trigger platform layout conflict risks includes: Define layout risk constraint rules based on the sensitive structures of local areas in the platform layout template; It should be noted that the platform's preset layout template is specifically the product information layout structure template stipulated by the target e-commerce platform. Preferably, it includes a product title area, a product promotion area, a logistics and delivery description area, and a product description area. Each layout area corresponds to different sensitivities in the platform's marketing strategy and SEO rules. For example, the promotion area is often a sensitive area under strict platform supervision and is more likely to trigger semantic conflict risks.
[0027] In a specific embodiment, based on historical statistics of violations triggered by product descriptions in different layout areas, the sensitive structures of each layout area in the platform layout template are determined. The process for determining the sensitive structures is as follows: First, extract high-frequency semantic structure patterns that trigger violations from historical layout violation records, such as the historical proportion of violations caused by "free delivery" and "big discount" being located in the same promotional area.
[0028] Then, these high-frequency violation semantic structure patterns are correlated with specific layout areas in the platform layout template to clarify the sensitivity weight values of each area.
[0029] For example, by calculating the frequency proportion of historical violation patterns occurring in each layout region. Define layout area Sensitivity weight for: In the formula, M represents the total number of regions in the layout template. For the j-th layout area in the template, This represents the frequency with which historical violation patterns occur in this area.
[0030] Furthermore, based on the sensitivity weight values of each layout region, a set of layout risk constraint rules is formed. ,in, This is a high-frequency risk semantic structure pattern.
[0031] The defined layout risk constraint rules include: High-risk semantic structure patterns are extracted based on historical layout violation data; Specifically, historical violation data includes known records of semantic unit combinations that led to platform penalties (removal from shelves, demotion) for products. In implementation, frequent pattern mining methods (such as Apriori or FP-Growth algorithms) are used to analyze historical violation data to obtain a set of frequently occurring risk semantic structure patterns. For example, it can be denoted as: In the formula, each mode This indicates a combination of at least two semantic units, such as "free shipping + big discount," which clearly constitutes a violation.
[0032] Layout risk constraint rules are constructed by associating high-risk semantic structure patterns with sensitive areas of layout templates. In specific implementation, based on the high-risk semantic structure pattern obtained above... Further, the distribution characteristics of these semantic patterns in different layout template regions are determined, and layout risk constraint rules are defined.
[0033] For example, the layout risk constraint rule is represented by the following triple: ;in, This is a high-risk semantic structure pattern. For sensitive areas in the layout template, To determine the strength of the condition that triggers a layout violation when this pattern appears in the region, historical violation data support is preferred, specifically defined as: ;in, For pattern In the region The number of times that led to violations in the past. For the region Total number of all violations within the organization.
[0034] By leveraging the structural mapping relationship between the initial set of semantic vectors and the layout risk constraint rules, semantic fragments that trigger platform layout conflict risks are extracted. It should be noted that the structural mapping relationship is not a simple semantic similarity matching, but a comprehensive mapping analysis that combines the structural positional relationship of semantic units in the product description, the semantic combination relationship, and the regional constraint relationship in the platform layout template, so as to ensure that the recognition results are interpretable and reproducible.
[0035] Specifically, the process of constructing the structural mapping relationship includes the following: First, based on the semantic unit segmentation results of the product description in the source language to be translated, an initial set of semantic vectors is obtained. Furthermore, by combining the expected placement of the product description in the source language within the platform layout template, each semantic unit is associated with its corresponding layout area, forming a semantic unit-layout area pair, denoted as: ,in, As a semantic unit, This refers to the layout area in the platform layout template.
[0036] Understandably, this association stems from the platform's rules for layouting different semantic content in product descriptions. For example, promotional semantics are usually placed in the promotion area, while logistics semantics are placed in the logistics description area.
[0037] Subsequently, for each semantic unit—layout region pair From the set of layout risk constraint rules Filtering the area that matches the layout A set of related high-risk semantic structure patterns.
[0038] In one specific embodiment, the semantic unit is determined. Whether to participate in a high-risk semantic structure pattern This is accomplished using semantic vector matching, which involves calculating semantic unit vectors. The similarity between the semantic unit vectors and the semantic structure pattern is specifically expressed as cosine similarity. ;in, It is the semantic vector of a semantic unit in a high-risk semantic structure pattern.
[0039] Preferably, when the semantic similarity is greater than a set threshold (e.g., 0.85), the semantic unit is determined. There is a matching relationship with the corresponding semantic unit in the risk semantic structure pattern.
[0040] Furthermore, to identify situations where multiple semantic unit combinations trigger risks, a joint mapping analysis is performed on the semantic unit combinations in the initial semantic vector set. Specifically: For the same layout area The set of semantic units within If the semantic unit combination is semantically similar to a high-risk semantic structure pattern If a match is found, the semantic unit combination is determined to be a semantic fragment that triggers the risk of layout conflict.
[0041] In some specific embodiments, the joint matching determination condition is defined as: Among them, when At that time, the corresponding semantic unit combination is identified as a semantic fragment that triggers the risk of layout conflict.
[0042] It should be further noted that, to ensure structural consistency in subsequent processing, the semantic fragments identified above will be uniformly structured and represented to form a set of layout risk semantic fragments, defined as: Each element contains: a set of semantic units contained in the semantic fragment, the corresponding layout region, and a matching high-risk semantic structure pattern identifier.
[0043] Understandably, this set not only records the semantic content that triggers layout conflict risks, but also retains the structural reasons for the risks, providing the necessary structural input for subsequent risk mitigation and replacement based on the semantic topology tree.
[0044] In one specific embodiment, when the product description to be translated contains two semantic units, "free shipping" and "bigdiscount", and both are mapped to the platform's promotional area, and both match a high-risk semantic structure pattern, then the combination is recorded as a risky semantic fragment and included in the risky semantic fragment set.
[0045] S103: Based on the semantic fragments that trigger the risk of platform layout conflict, construct a semantic topology tree that adapts to the platform layout requirements by combining the initial semantic vector set, and generate semantic unit replacement combinations that can eliminate the risk of layout conflict according to the semantic conflict pattern. Specifically, the construction of a semantic topology tree adapted to the platform layout requirements includes: Based on the semantic layout relationships between semantic fragments that trigger platform layout conflict risks, a layout risk propagation graph is constructed. It should be noted that the layout risk propagation graph is specifically a directed weighted graph structure with semantic units as nodes and layout risk propagation relationships as edges. The purpose of constructing this propagation graph is to clearly depict the risk association relationships between different semantic segments, thereby providing a foundation for subsequently determining the risk contribution of each semantic unit.
[0046] In a specific embodiment, the set of semantic fragments that trigger risks is first obtained in S102. Extract all semantic units involved in layout risks and denote them as a node set. .
[0047] Furthermore, through statistical analysis of historical violation data, any two semantic units are identified. and The frequency of simultaneous occurrences in violation events is used to define the edge weight of risk propagation between nodes. ;in, Semantic units representing historical layout violation events and The number of times they appear at the same time This represents the total number of all layout violations.
[0048] The complete layout risk propagation diagram can be obtained through the above methods: ;in, It is a set of edges formed based on the risk propagation relationship between semantic units. Let be the set of edge weights.
[0049] Based on the layout risk propagation path of the nodes in the layout risk propagation diagram, determine the layout risk contribution of each node. The determination of the layout risk contribution of the node includes: Based on the semantic propagation strength of nodes in historical layout violation incidents, obtain the node propagation risk weight; It is understandable that different nodes (semantic units) play different roles in layout violation events, so it is necessary to calculate the risk propagation intensity of each node in historical violation events.
[0050] In practice, the propagation risk weight of a node is defined as the centrality value of the node in the layout risk propagation graph, preferably represented by eigenvector centrality. The calculation formula is: ;in, For the image The largest eigenvalue was determined based on experimental data. Represents a node The set of neighboring nodes, The edge weight is denoted as .
[0051] By iteratively solving the eigenvector centrality calculation formula, the propagation risk weight corresponding to each node can be obtained.
[0052] The contribution of node layout risk is calculated by combining the risk weight of node propagation with the density of node topological connections in the layout risk propagation diagram. It should be noted that the layout risk contribution rate takes into account both the propagation weight of a node in the risk propagation process and the topological connection structure of the node in the layout risk propagation graph, thereby ensuring that the determined layout risk contribution rate can accurately reflect the degree of influence of the node on the overall layout risk.
[0053] Specifically, the risk contribution of node layout The calculation formula is defined as follows: ;in, Propagate risk weights to nodes. For nodes In the layout of the risk propagation diagram, the out-degree (i.e., from node) (Number of starting edges) It is the sum of the out-degrees of all nodes.
[0054] The above definition of layout risk contribution indicates that if a node has both a high propagation risk weight and a large out-degree, it means that its risk propagation intensity is high and it can affect more other nodes. Therefore, the layout risk contribution of this node is relatively large.
[0055] Optimize the node connection structure based on the risk contribution of node layout to generate a semantic topology tree that adapts to the platform layout requirements; Specifically, the semantic topology tree is a tree structure constructed by optimizing the node connection relationship in the layout risk propagation graph. It can clearly reflect the hierarchy and propagation path of semantic units in the platform layout risk, thereby providing a clear structural basis for the subsequent generation of risk elimination solutions.
[0056] In one specific embodiment, the process of constructing a semantic topology tree is as follows: First, determine the root node of the semantic topology tree. Preferably, the node with the highest contribution to layout risk is set as the root node of the semantic topology tree, representing the main source of layout risk propagation.
[0057] Then, based on the order of layout risk contribution from high to low, other nodes are connected to the existing tree structure level by level. When adding a new node, the edge weights in the layout risk propagation graph and the layout risk contribution are used as the criteria for connection judgment. The connection rule is defined as: if the node... The risk contribution of the layout is second only to the node. And nodes With nodes In a risk propagation graph with direct edge connections, the nodes will be... As a node The child nodes.
[0058] It should be noted that if there are multiple possible parent nodes to choose from, the parent node with a higher contribution to the layout risk and a larger edge weight is preferred, so as to maximize the reflection of the core path of layout risk propagation.
[0059] Through the above connection optimization methods, the semantic topology tree structure is finally obtained: ;in, For a set of nodes, This is the optimized set of edge connections.
[0060] It should be noted that the semantic topology tree provides a clear structural basis for the subsequent generation of semantic unit replacement combinations that can eliminate the risk of layout conflicts.
[0061] Specifically, the generation of semantic unit replacement combinations capable of eliminating layout conflict risks includes: Based on the layout risk contribution of the semantic topology tree, determine the set of key nodes for layout risk; It should be noted that the purpose of determining the set of key nodes for layout risk is to select the set of nodes in the semantic topology tree that make a significant contribution to layout risk and whose replacement can effectively reduce the overall layout risk.
[0062] In practice, it is preferable to sort nodes according to their contribution to layout risk, and select nodes from the semantic topology tree that rank in the top 20% (e.g., the top 20%) to form a set of key nodes for layout risk. ;in, This represents the set of the top K nodes after ranking them by their contribution to the risk of node layout.
[0063] For example, suppose that after sorting the nodes of the topology tree by their contribution to the layout risk, the top 3 nodes are "free shipping", "big discount" and "fast delivery" to form a set of key nodes for layout risk, which will be used for subsequent risk replacement processing.
[0064] Based on the semantic features of key nodes with layout risks, semantic candidate units with low layout risks are mined from historical data. It should be noted that the semantic alternative units with low layout risk in this step are semantic units that are semantically similar to the key nodes of layout risk in historical data, but have not appeared frequently in historical layout violation events, so as to ensure that the product description after replacement is still semantically fluent and has low layout risk.
[0065] In practice, the first step is to identify key risk points in the layout. Semantic feature vector representation is performed, and then candidate semantic units are mined from the set of safe semantic units that have not frequently caused violations in historical data through semantic similarity matching.
[0066] Specifically, a set of semantic candidate units with low deployment risk. Defined as: ;in, The cosine similarity between semantic risk key nodes and historical security semantic units. The similarity threshold is determined based on experimental data, for example, set to 0.8. This is a set of low-risk semantic units from historical data.
[0067] For example, taking "free shipping" as a key node for layout risk, the semantic candidate units "shipping discount" or "low-cost shipping" with high similarity are matched from the historical low-risk semantic unit set for subsequent combination replacement.
[0068] Based on the semantic compatibility and layout constraint rules among the candidate units, a multi-objective combinatorial optimization algorithm is used to generate semantic unit replacement combinations that can eliminate the risk of layout conflicts. It should be noted that the goal of this step is to effectively eliminate layout risks in the original product description by generating reasonable semantic unit replacement combinations, while maintaining the semantic coherence and marketing effectiveness of the product description to the greatest extent possible. Therefore, multi-objective optimization needs to be carried out by fully considering the semantic compatibility between semantic candidate units and platform layout constraints.
[0069] In a specific embodiment, the multi-objective combinatorial optimization algorithm preferably employs NSGA-II (non-dominated sorting genetic algorithm), and the specific process is disclosed as follows: First, the decision variables for the multi-objective optimization problem are defined as the set of critical nodes for layout risk. The alternative semantic units corresponding to each key node are represented by the decision variables as follows: ;in, This represents the alternative semantic unit selected for the i-th critical node of layout risk.
[0070] Furthermore, a multi-objective optimization function is defined, comprising the following two objective functions: Objective function 1: Maximize the degree of layout risk elimination. Layout risk elimination Defined as the difference between the total risk contribution of the original layout and the total risk contribution of the replaced layout: ; Objective function 2: Semantic compatibility (maximization) Semantic compatibility function , defined as the average semantic similarity between the candidate semantic unit and the replaced layout risk key node: ;in, For the original key node With replacement unit Cosine similarity between semantic vectors.
[0071] Based on the above definition, the multi-objective optimization problem of semantic unit substitution and combination can be expressed as: The constraints are the layout constraint rules of the platform layout template, specifically: It should be noted that the constraints require that the replaced semantic unit combination must meet the platform's layout template area's specification requirements for semantic unit combinations, that is, the candidate unit combination cannot frequently trigger violation events in historical data, and the semantics must be reasonable.
[0072] In practical implementation, the NSGA-II algorithm is used for optimization. The algorithm process is as follows: Initialize the population: Randomly generate an initial population of candidate semantic unit combinations, with the population size preferably being 50 to 100; Non-dominated sorting: Non-dominated sorting is performed based on the objective function value to determine the Pareto front rank of each solution in the population; Crossover and mutation: A binary tournament selection method is used to select high-quality individuals for crossover and mutation operations to generate new solution combinations; Population renewal: Combine the old and new populations to perform a new non-dominated sorting, retaining solutions with higher frontier levels to form the next generation population; Termination condition: The above optimization process is repeated iteratively until the maximum number of iterations (e.g., 100 generations) is reached, and the final Pareto optimal solution set is obtained.
[0073] It should be noted that the solution with better layout risk elimination and semantic compatibility is finally selected from the Pareto optimal solution set as the final semantic unit replacement combination.
[0074] For example, for the set of key risk nodes in the layout {"free shipping", "big discount"}, the above multi-objective optimization algorithm finally obtains the optimal combination of "low-cost shipping" and "limited discount" to replace the original high-risk combination "free shipping + big discount".
[0075] S104: Based on the semantic topology tree, the semantic units are replaced and combined to reorganize the layout and generate multilingual product description text that conforms to the SEO layout rules of the target e-commerce platform; Specifically, the layout reorganization of semantic unit replacement combinations based on the semantic topology tree includes: The set of candidate layout paths for semantic unit replacement combinations is determined based on the semantic topology tree; It should be noted that the candidate layout path set specifically represents the possible layout order of semantic unit replacement combinations in the product description text. This set is determined based on the parent-child relationships between nodes in the semantic topology tree to ensure that the replaced product description text conforms to the platform's layout requirements while reducing layout risks.
[0076] In a specific embodiment, the semantic topology tree is first used as the starting point for layout, and the paths are expanded sequentially according to the top-down tree structure. Preferably, a depth-first search (DFS) method is used to traverse the semantic topology tree to generate all possible layout paths from the root node to the leaf node.
[0077] For example, suppose the semantic topology tree structure is as follows: Root node: "low-cost shipping" Second-level node: "limited discount" The third layer of nodes: "fast delivery" and "quality assurance" Then, the DFS method is used to traverse the semantic topology tree to obtain the set of candidate layout paths: .
[0078] The target layout path is obtained by evaluating the compatibility between the candidate layout path set and the layout rules of the target e-commerce platform. Specifically, the adaptability evaluation of the layout path involves assessing each path in the candidate layout path set to select the path that best meets the platform's SEO layout requirements.
[0079] It should be noted that the compatibility evaluation includes the following two indicators: Layout compliance Defined as a candidate path The degree of matching with the platform layout template's preset structure is specifically represented by the conformity between the path semantic unit position and the platform layout area, and is defined as follows: ;in, Representing a path The j-th semantic unit in This represents the corresponding area in the platform layout template, function. The score represents the matching score. If the semantic unit matches the layout area, it is recorded as 1; otherwise, it is recorded as 0.
[0080] Semantic coherence , defined as the average semantic similarity between adjacent semantic units in the path, to ensure the semantic coherence of the replaced product description. The specific calculation formula is as follows: ;in, It represents the cosine similarity of adjacent semantic unit vectors in the path.
[0081] Furthermore, a comprehensive fitness scoring function is defined: ;in, The weighting factor is determined based on experimental data. For example, setting it to 0.6 indicates that the importance of platform layout compliance is slightly higher than semantic coherence.
[0082] Furthermore, the above scoring is applied to all paths in the candidate layout path set, and the path with the highest comprehensive score is selected as the target layout path: ; For example, assuming that after scoring calculation, the path "low-cost shipping → limited discount → fast delivery" receives the highest overall score, then this path is determined as the target layout path.
[0083] Based on the target layout path, semantic units are replaced and combined to reorganize the layout, generating multilingual product description text that conforms to the SEO layout rules of the target e-commerce platform. It should be noted that the goal of this embodiment is to further map the aforementioned determined target layout path into the platform's preset layout template, thereby forming the final multilingual product description text. This layout reorganization process comprehensively considers the semantic rationality of the product description, platform marketing requirements, and layout compliance constraints.
[0084] In practice, the restructuring process is as follows: First, based on the target layout path The region mapping relationship between semantic units and platform layout templates determines the layout position of each semantic unit in the layout template.
[0085] For example, in one specific embodiment, the layout template includes the following areas: product title area (Title), promotion area (Promotion), shipping description area (Shipping), and product description area (Description). Taking the target layout path "low-cost shipping→limited discount→fast delivery" as an example, the layout position correspondence can be specifically as follows: “low-cost shipping” is mapped to the logistics and delivery description area; “Limited discount” is mapped to the promotional area; "Fast delivery" is preferably mapped to the logistics delivery description area or the product description area (the specific mapping is determined based on the platform's historical best layout experience).
[0086] Furthermore, the aforementioned semantic units and their corresponding layout areas are concatenated and recombined using text formatting. Specifically, the text recombination rules for semantic units within each area are as follows: Layout of individual semantic units within a region: The corresponding text of the semantic unit can be directly inserted into the standard description format of the relevant area. For example, the semantic unit "low-cost shipping" in the logistics and distribution description area can be expressed as "Enjoy low-cost shipping on your orders".
[0087] Layout of multiple semantic units within a region: The optimal word order, determined by the platform's historical experience, is used for concatenation to ensure semantic fluency and maximize marketing effectiveness. For example, when combining multiple semantic units within a promotional area, they are typically ordered from highest to lowest according to the level of discount or promotional intensity.
[0088] For example, for the target layout path "low-cost shipping→limited discount→fast delivery" mentioned above, the product description text is generated as follows: Product title area (Title): No special promotional information is included; retain the original brand and product name. Promotion section: "Enjoy a limited discount now!"; Shipping description area: "Enjoy low-cost shipping with fast delivery options available." Product Description Area: This area does not include the aforementioned promotional semantic units; it retains the original product detail description text.
[0089] This generates multilingual product description text that conforms to the SEO layout rules of the target e-commerce platform.
[0090] It should be noted that, in order to optimize the translation of multilingual product descriptions, the text that has been restructured and reorganized will be further submitted to a pre-set multilingual translation engine or a platform-based human translation review process to ensure that the text in each target language simultaneously meets the platform's layout requirements and the optimization goals of marketing effectiveness.
[0091] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.
Claims
1. A SEO optimization method based on multilingual product description translation, characterized in that, include: Based on the historical multilingual product description data of the target e-commerce platform, we can mine semantic conflict patterns that are sensitive to platform layout, and generate an initial set of semantic vectors by combining the product descriptions in the source language to be translated. Based on the platform's preset layout template, the structural mapping relationship between the initial semantic vector set and the semantic conflict pattern is determined, and the semantic fragments in the initial semantic vector set that trigger the risk of platform layout conflict are identified. Based on the semantic fragments that trigger platform layout conflict risks, a semantic topology tree adapted to platform layout requirements is constructed by combining the initial semantic vector set, and semantic unit replacement combinations that can eliminate layout conflict risks are generated according to the semantic conflict pattern. Based on the semantic topology tree, semantic units are replaced and combined to reorganize the layout, generating multilingual product description text that conforms to the SEO layout rules of the target e-commerce platform.
2. The SEO optimization method based on multilingual product description translation according to claim 1, characterized in that, The mining platform is configured with semantically sensitive conflict patterns, including: Based on historical multilingual product description data, a cross-lingual semantic co-occurrence network is generated; Based on the topological stability change trend of co-occurring network nodes, identify layout-sensitive abnormal nodes; Based on the semantic conflict structural characteristics of layout-sensitive abnormal nodes, clustering is used to form layout-sensitive semantic conflict patterns.
3. The SEO optimization method based on multilingual product description translation according to claim 2, characterized in that, The determination of layout-sensitive abnormal nodes includes: Analyze the topology changes of co-occurring network nodes before and after historical layout violation events to identify nodes with topology changes; Based on the correlation between the semantic features of topology change nodes and the platform's layout violation history, layout-sensitive abnormal nodes are identified.
4. The SEO optimization method based on multilingual product description translation according to claim 3, characterized in that, The semantic fragments in the initial semantic vector set that trigger platform layout conflict risks include: Define layout risk constraint rules based on the sensitive structures of local areas in the platform layout template; By leveraging the structural mapping relationship between the initial set of semantic vectors and the layout risk constraint rules, semantic fragments that trigger platform layout conflict risks are extracted.
5. The SEO optimization method based on multilingual product description translation according to claim 4, characterized in that, The defined layout risk constraint rules include: High-risk semantic structure patterns are extracted based on historical layout violation data; Layout risk constraint rules are constructed by associating high-risk semantic structure patterns with sensitive areas of layout templates.
6. The SEO optimization method based on multilingual product description translation according to claim 5, characterized in that, The construction of a semantic topology tree adapted to the platform layout requirements includes: Based on the semantic layout relationships between semantic fragments that trigger platform layout conflict risks, a layout risk propagation graph is constructed. Based on the layout risk propagation path of the nodes in the layout risk propagation diagram, determine the layout risk contribution of each node. Optimize the node connection structure based on the risk contribution of node layout to generate a semantic topology tree that adapts to the platform layout requirements.
7. The SEO optimization method based on multilingual product description translation according to claim 6, characterized in that, The contribution of the determined node to the layout risk includes: Based on the semantic propagation strength of nodes in historical layout violation incidents, obtain the node propagation risk weight; The contribution of node layout risk is calculated by using the risk weight of node propagation and the density of node topological connections in the layout risk propagation diagram.
8. The SEO optimization method based on multilingual product description translation according to claim 7, characterized in that, The generation of semantic unit replacement combinations capable of eliminating layout conflict risks includes: Based on the layout risk contribution of the semantic topology tree, determine the set of key nodes for layout risk; Based on the semantic features of key nodes with layout risks, semantic candidate units with low layout risks are mined from historical data. Based on the semantic compatibility and layout constraint rules among candidate units, a multi-objective combinatorial optimization algorithm is used to generate semantic unit replacement combinations that can eliminate the risk of layout conflicts.
9. The SEO optimization method based on multilingual product description translation according to claim 8, characterized in that, The layout reorganization of semantic unit replacement combinations based on the semantic topology tree includes: The set of candidate layout paths for semantic unit replacement combinations is determined based on the semantic topology tree; The target layout path is obtained by evaluating the compatibility between the candidate layout path set and the layout rules of the target e-commerce platform. Based on the target layout path, semantic units are replaced and combined to reorganize the layout, generating multilingual product description text that conforms to the SEO layout rules of the target e-commerce platform.