A digital marketing advertisement script automatic generation method and system

Abstract

This invention discloses a method and system for automatically generating digital marketing advertising copy, relating to the field of big data analysis. The method includes: parsing original requirements through natural language processing, extracting keywords, and matching them with industry tags to form a structured list; constructing a dynamic material library and establishing an update mechanism, combining the requirement list with personalized generation strategies; generating multiple versions of initial copy drafts based on a semantic framework, and iteratively optimizing them through compliance, adaptability, and dissemination potential after sentiment calibration and logical integration; using a prediction model trained on historical data to customize the copy for different channels; and finally outputting multi-channel adapted versions, optimizing the prediction model through real-time delivery data feedback. The advantages of this invention are: by accurately parsing marketing requirements, dynamically updating the material library, and combining personalized strategy generation with multi-dimensional optimization, it achieves automated production of multi-channel adapted copy, efficiently adapting to diverse marketing scenarios while significantly reducing costs and increasing efficiency.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, and in particular to a method and system for automatically generating digital marketing advertising copy. Background Technology

[0002] With the rapid development of the digital economy, enterprises' demand for precise and personalized marketing has grown dramatically. Traditional methods of manually writing advertising copy are no longer sufficient to meet the needs of massive and high-frequency campaigns. On the one hand, the explosive growth of consumer behavior data and the increasingly refined user profiles require advertising copy to dynamically adapt to different scenarios and audiences. On the other hand, breakthroughs in natural language processing technology, especially the emergence of pre-trained large models, have provided powerful semantic understanding and content creation capabilities for text generation.

[0003] The core disadvantages of current automated digital marketing copy generation methods lie in their demand analysis capabilities. They rely heavily on vague instructions rather than structured analysis, lacking sufficient semantic mining of unstructured demand text, which can easily lead to deviations in brand value delivery or omissions of core appeals. Furthermore, their material libraries are mostly static template-based, lacking industry-specific material categorization and real-time trend updates, resulting in severe copy homogenization and a lack of timeliness. Personalization capabilities are weak, making it difficult to accurately match style and format strategies based on customer groups, scenarios, and channels, resulting in highly generic but insufficiently targeted content. Simultaneously, they lack a multi-dimensional, integrated optimization and evaluation system, with incomplete compliance verification and dissemination potential assessment, and generally lack a closed-loop feedback loop for campaign data, hindering dynamic iteration of generation strategies and making it difficult to guarantee dissemination effectiveness and marketing conversion efficiency across different channels. Summary of the Invention

[0004] To improve existing methods and systems, this paper presents a method and system for automatically generating digital marketing advertising copy. This method achieves automated production of copy that adapts to multiple channels by accurately analyzing marketing needs, dynamically updating the material library, and combining personalized strategy generation with multi-dimensional optimization. Furthermore, it relies on a data feedback loop for continuous iteration, effectively adapting to diverse marketing scenarios while significantly reducing costs and increasing efficiency.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for automatically generating digital marketing advertising copy includes: Receive digital marketing demand information, parse unstructured text through natural language processing, extract keywords and semantic relationships, match industry standard tags to generate a demand matrix, and output a structured demand list after rationality verification; Based on the demand matrix, a dynamic material library is built, which includes basic materials, industry-specific materials, and real-time hot topics. A material update mechanism is established to update the knowledge base materials according to industry dynamics, seasonal changes, and the life cycle of hot topics. Based on a structured list of requirements and a dynamic material library, the length, information density, and presentation format of the copy are matched according to the type of marketing scenario and the communication channel. The value delivery strategy is matched in combination with marketing appeal tags, and the matched style, format, and value delivery strategy are integrated into a personalized generation strategy solution. Based on a personalized generation strategy, a core semantic framework for copywriting is constructed according to a value delivery strategy. Based on style and form strategies, copywriting fragments with different sentence structures are generated. The generated copywriting fragments are then calibrated in terms of emotional tone. By logically integrating the copywriting fragments, initial drafts of copywriting with different focuses are obtained. Build a copywriting optimization evaluation system that considers compliance with changes, marketing adaptability, and communication potential. Optimize the initial drafts of the copy in a targeted manner and score them quantitatively. If the copy does not meet the standards, adjust the generation strategy until the optimized copy meets the standards. Based on historical marketing copy dissemination data, an effect prediction model is built to predict the dissemination effect of the copy on various channels, and the optimized copy is adjusted accordingly to generate customized versions adapted to different dissemination channels. The generated customized copy for each channel is integrated and output, the actual dissemination data after the copy is launched is collected in real time, and compared with the predicted effect to generate the effect difference results and update the effect prediction model parameters.

[0006] Preferably, the process of receiving digital marketing demand information, parsing unstructured text through natural language processing, extracting keywords and semantic relationships, matching industry standard tags to generate a demand matrix, and outputting a structured demand list after rationality verification specifically includes: Receive users' core digital marketing needs information and classify it into structured and unstructured types, including product attribute parameters, target customer profiles, marketing scenario types, core marketing appeals, and communication channel attributes; Natural language processing techniques are used to segment, tag, and perform dependency parsing on unstructured text to extract key terms and semantic relationships. The system invokes a pre-defined industry marketing tagging system to match extracted keywords with product, target audience, scenario, demand, and channel tags, generating a multi-dimensional standardized demand matrix. The standardized demand matrix is ​​validated for rationality, contradictory labels are removed, and missing key demand data is supplemented to output a structured marketing demand list.

[0007] Preferably, the construction of a dynamic material library based on a demand matrix, including basic materials, industry-specific materials, and real-time trending materials, and the establishment of a material update mechanism to update the knowledge base materials according to industry dynamics, seasonal changes, and the life cycle of trending topics, specifically includes: Based on a standardized demand matrix, a knowledge base architecture is built that includes basic materials, industry-specific materials, and real-time trending materials, with preset material classification fields and association rules for each module. The basic materials include general marketing script templates and common product description terms, which are categorized and archived according to marketing scenarios and appeal types. Industry-specific materials include marketing copy and product terminology from various industries, which are crawled using web scraping technology. Real-time trending materials include trending elements from mainstream news platforms and social media trending lists. Establish a material update mechanism to automatically update the material library based on industry trends, seasonal changes, and the life cycle of trending topics, and record historical usage data of the materials.

[0008] Preferably, the process of matching copy length, information density, and presentation format based on a structured demand list and dynamic material library, according to marketing scenario type and communication channel, and combining marketing appeal tags to match value delivery strategies, and integrating the matched style, format, and value delivery strategies into a personalized generation strategy solution specifically includes: Based on a structured marketing needs list and a dynamic material library, a copywriting generation strategy matching model is constructed. Based on the target customer profile tags, a language style matching strategy is adopted, including tone intensity, vocabulary preferences and sentence structure, and differentiated style templates are preset for different age groups and consumption levels. Match copy length, information density, and presentation format based on marketing scenario type and communication channel attributes; By combining core marketing appeal tags with value delivery strategies, we can identify the core selling points and emotional value highlighted in the copywriting, and integrate the matched style, form, and value delivery strategies into a personalized strategy solution.

[0009] Preferably, the personalized generation strategy involves constructing a core semantic framework for the copy based on a value delivery strategy, generating copy fragments with different sentence structures according to style and form strategies, calibrating the emotional tone of the generated copy fragments, and obtaining initial drafts of copy with different emphases through logical integration of the copy fragments. Specifically, this includes: Based on the personalized generation strategy, semantic construction deconstructs the core of value delivery and combines it with related materials in the material library to build a three-level semantic framework of core selling points, supporting arguments, and emotional resonance points. Sentence generation uses style and form strategies to call up vocabulary from basic materials, fill in the content of a three-level semantic framework, and generate multiple differentiated sentence fragments. Emotional optimization calibrates the emotional tone of a segment by matching emotional vocabulary and adjusting modal particles. The fragments were integrated according to semantic logic association rules to form three initial drafts with different focuses, namely product functions, emotional value, and benefits.

[0010] Preferably, the construction of a copywriting optimization evaluation system that assesses compliance with changes, marketing adaptability, and dissemination potential, involves targeted optimization of the initial copywriting draft, quantitative scoring, and adjustments to the generation strategy if the draft fails to meet the standards, until a satisfactory optimized copywriting is obtained. Specifically, this includes: Construct a three-tiered evaluation system that includes compliance, marketing suitability, and dissemination potential. The compliance indicator is based on a pre-set list of prohibited words and rules. The marketing suitability indicator sets a threshold for tag matching and a standard for the accuracy of conveying core selling points. The dissemination potential indicator establishes a readability threshold and attractive keywords. Based on the quantitative scores output by the evaluation system, the initial draft of the copy is optimized in a targeted manner, including replacing prohibited words, strengthening core information, and adjusting the language style; If the optimized copy still has items that do not meet the scoring criteria, the generation strategy will be readjusted until optimized copy that meets the evaluation criteria is generated.

[0011] Preferably, the step of constructing an effect prediction model based on historical marketing copy dissemination data to predict the dissemination effect of the copy on various channels, making differentiated adjustments to the optimized copy, and generating customized versions adapted to different dissemination channels specifically includes: Call the effect prediction model trained based on historical marketing copy communication data, and import the optimized copy and structured requirement list; The model breaks down and optimizes the core information, language style, and emotional orientation of copywriting, and predicts key performance indicators such as exposure, click-through rate, and conversion potential under different communication channels. Based on the predicted results, targeted adjustments were made to the optimized copy to generate customized versions suitable for different communication channels. These included streamlining the text to within 30 characters and adding interactive prompts for social media channels, strengthening the density of core keywords and highlighting benefits for search engine marketing channels, and adding a scenario-based narrative opening for long video channels.

[0012] Preferably, the step of integrating and outputting the generated customized copy for each channel, collecting actual dissemination data after the copy is deployed in real time, comparing it with the predicted effect, generating effect difference results, and updating the effect prediction model parameters specifically includes: Establish a full lifecycle tracking mechanism for copywriting, connect to data interfaces of various communication channels, collect actual dissemination data such as exposure, click-through rate, and conversion rate after copywriting is launched in real time, and generate results on performance differences by comparing them with predicted performance indicators. Based on the discrepancies and historical feedback data, the effect prediction model was updated, the parameters of the evaluation system were optimized, and the triggering conditions for the update mechanism of the material library were simultaneously optimized.

[0013] Furthermore, a digital marketing advertising copy automated generation system is proposed, including: Demand Analysis Module: Uses natural language processing technology to analyze unstructured marketing demands input by users, extract keywords and match industry tags, and generate a standardized demand matrix and structured list; Dynamic Material Library Module: Constructs a multi-dimensional material library containing basic scripts, industry-specific content, and real-time hot topics, and updates material data based on industry dynamics, seasons, and hot topic cycles; Strategy generation module: Based on the requirements list and material library, match the copywriting style, format and value delivery strategy, and output a personalized generated solution; Copy generation module: Based on the strategy, a semantic framework is built to generate multiple draft versions, and emotional calibration and logical integration are performed to output copy with different focuses such as function, emotion, and discounts; Intelligent optimization module: The initial draft is quantitatively scored and optimized through a three-dimensional evaluation system of compliance, adaptability, and dissemination potential. If it fails to meet the standards, the generation strategy is iteratively adjusted until it meets the standards. Channel adaptation module: Utilizes performance prediction models to adjust copywriting style for different channels and generate customized versions; Data feedback module: Collects data in real time and compares it with the prediction results, updates model parameters and material library; Processor: The processor is used to handle the calculation process of each formula and the construction calculation process of each model.

[0014] Compared with the prior art, the advantages of the present invention are: By leveraging NLP technology, unstructured needs can be accurately analyzed and standardized, ensuring accurate and efficient demand matching. A dynamic material library, combined with a real-time update mechanism, provides fresh and suitable material support for copywriting generation. Personalized solutions are generated based on multi-dimensional strategy matching, producing drafts with multiple focuses. Accurate optimization is achieved through a three-dimensional evaluation system of compliance, adaptability, and dissemination potential, ensuring copywriting quality. Channel-differentiated adaptation is achieved using performance prediction models, enhancing the targeting of communication. Simultaneously, a full lifecycle data tracking and model iteration mechanism forms a closed loop of "generation-deployment-feedback-optimization," reducing labor costs, improving generation efficiency, and continuously optimizing copywriting effectiveness to adapt to the marketing needs of different industries, scenarios, and channels. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method proposed in this invention; Figure 2 This is a schematic diagram illustrating the marketing needs analysis proposed in this invention; Figure 3 This is a schematic diagram illustrating the construction and updating of the marketing material library proposed in this invention; Figure 4 This is a schematic diagram illustrating the dynamic matching of the personalized copywriting generation strategy proposed in this invention. Figure 5 This is a schematic diagram illustrating the generation of the initial draft of the document proposed in this invention; Figure 6 This is a schematic diagram illustrating the data copy optimization iteration proposed in this invention; Figure 7 This is a schematic diagram illustrating the marketing effect prediction and differentiated version generation proposed in this invention. Figure 8 This is a schematic diagram illustrating the dynamic iterative update of the text proposed in this invention. Detailed Implementation

[0016] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0017] A digital marketing advertising copy automated generation system includes: Demand Analysis Module: Uses natural language processing technology to analyze unstructured marketing demands input by users, extract keywords and match industry tags, and generate a standardized demand matrix and structured list; Dynamic Material Library Module: Constructs a multi-dimensional material library containing basic scripts, industry-specific content, and real-time hot topics, and updates material data based on industry dynamics, seasons, and hot topic cycles; Strategy generation module: Based on the requirements list and material library, match the copywriting style, format and value delivery strategy, and output a personalized generated solution; Copy generation module: Based on the strategy, a semantic framework is built to generate multiple draft versions, and emotional calibration and logical integration are performed to output copy with different focuses such as function, emotion, and discounts; Intelligent optimization module: The initial draft is quantitatively scored and optimized through a three-dimensional evaluation system of compliance, adaptability, and dissemination potential. If it fails to meet the standards, the generation strategy is iteratively adjusted until it meets the standards. Channel adaptation module: Utilizes performance prediction models to adjust copywriting style for different channels and generate customized versions; Data feedback module: Collects data in real time and compares it with the prediction results, updates model parameters and material library; Processor: The processor is used to handle the calculation process of each formula and the construction calculation process of each model.

[0018] See Figure 1 As shown, a method for automatically generating digital marketing advertising copy includes: Step 1: Receive digital marketing demand information, parse unstructured text through natural language processing, extract keywords and semantic relationships, match industry standard tags to generate a demand matrix, and output a structured demand list after rationality verification. Step 2: Based on the demand matrix, build a dynamic material library containing basic materials, industry-specific materials, and real-time hot topics. Establish a material update mechanism to update the knowledge base materials according to industry dynamics, seasonal changes, and the life cycle of hot topics. Step 3: Based on the structured requirements list and dynamic material library, match the copy length, information density and presentation format according to the marketing scenario type and communication channel, and match the value delivery strategy with marketing appeal tags. Integrate the matched style, form and value delivery strategy into a personalized strategy solution. Step 4: Based on the personalized generation strategy, construct the core semantic framework of the copy according to the value delivery strategy, generate copy fragments with different sentence structures according to the style and form strategy, calibrate the emotional tone of the generated copy fragments, and obtain the first drafts of copy with different focuses through the logical integration of the copy fragments. Step 5: Build a copywriting optimization evaluation system that considers compliance with changes, marketing adaptability, and communication potential. Optimize the initial draft copy in a targeted manner and score it quantitatively. If it does not meet the standards, adjust the generation strategy until a satisfactory optimized copy is obtained. Step Six: Build an effectiveness prediction model based on historical marketing copy dissemination data to predict the dissemination effect of the copy on various channels, make differentiated adjustments to the optimized copy, and generate customized versions adapted to different dissemination channels; Step 7: Integrate and output the generated customized copy for each channel, collect the actual dissemination data after the copy is launched in real time, compare it with the predicted effect, generate the effect difference results, and update the effect prediction model parameters.

[0019] See Figure 2 As shown, the system receives digital marketing demand information, parses unstructured text using natural language processing, extracts keywords and semantic relationships, matches them with industry-standard tags to generate a demand matrix, and outputs a structured demand list after rationality verification. This list specifically includes: Receive users' core digital marketing needs information and classify it into structured and unstructured types, including product attribute parameters, target customer profiles, marketing scenario types, core marketing appeals, and communication channel attributes; Natural language processing techniques are used to segment, tag, and perform dependency parsing on unstructured text to extract key terms and semantic relationships. The system invokes a pre-defined industry marketing tagging system to match extracted keywords with product, target audience, scenario, demand, and channel tags, generating a multi-dimensional standardized demand matrix. The standardized demand matrix is ​​validated for rationality, contradictory labels are removed, and missing key demand data is supplemented to output a structured marketing demand list.

[0020] Specifically, a natural language processing model based on the Transformer architecture is used to perform deep semantic analysis on the normalized requirement text. First, word segmentation is performed, and the segmentation results are optimized by combining a marketing-specific thesaurus to avoid splitting core marketing terms. Then, part-of-speech tagging and named entity recognition are performed to accurately locate core entities such as product names, customer tags, and scenario keywords. Through dependency parsing and semantic role labeling, a semantic dependency tree of the requirement text is constructed to clarify the logical relationships between keywords. Finally, a keyword weight calculation model is used to select core requirement keywords and secondary requirement keywords to form preliminary requirement analysis results. A standardized marketing tag system covering the entire industry is pre-built. This system includes five dimensions: product tags, audience tags, scenario tags, appeal tags, and channel tags. Each dimension contains multi-level sub-tags and tag attribute descriptions. The core keywords obtained from semantic parsing are compared with the standardized tag system, and a multi-layer matching strategy of "precise matching + fuzzy matching + semantic similarity matching" is adopted. A multi-dimensional standardized demand matrix is ​​constructed based on the matching results.

[0021] See Figure 3 As shown, a dynamic resource library containing basic materials, industry-specific materials, and real-time trending materials is constructed based on a demand matrix. A material update mechanism is established to update the knowledge base materials according to industry dynamics, seasonal changes, and the life cycle of trending topics. Specifically, this includes: Based on a standardized demand matrix, a knowledge base architecture is built that includes basic materials, industry-specific materials, and real-time trending materials, with preset material classification fields and association rules for each module. The basic materials include general marketing script templates and common product description terms, which are categorized and archived according to marketing scenarios and appeal types. Industry-specific materials include marketing copy and product terminology from various industries, which are crawled using web scraping technology. Real-time trending materials include trending elements from mainstream news platforms and social media trending lists. Establish a material update mechanism to automatically update the material library based on industry trends, seasonal changes, and the life cycle of trending topics, and record historical usage data of the materials.

[0022] Specifically, establish a tag mapping relationship between materials and a standardized demand matrix, supplement each material with multi-dimensional matching tags such as product, target audience, and channel, forming a two-way association index of "material-tag"; optimize the material retrieval algorithm, and construct multi-level retrieval and ranking rules by combining tag matching degree, material historical usage effect score, timeliness and other dimensions. Set up trigger conditions for material updates, with the basic material module updated quarterly, the industry-specific material module updated monthly, and the real-time hot material module updated hourly; at the same time, it triggers instant updates based on industry dynamic early warning signals, seasonal changes, and hot life cycle nodes; establish a material quality control model to track the historical usage effect data of each material in real time, downgrade or eliminate low-performance materials, and increase the recommendation weight of high-performance materials.

[0023] See Figure 4 As shown, based on a structured requirements list and a dynamic material library, the copy length, information density, and presentation format are matched according to the marketing scenario type and communication channel. Combined with marketing appeal tags, a value delivery strategy is matched, and the matched style, format, and value delivery strategy are integrated into a personalized strategy solution, specifically including: Based on a structured marketing needs list and a dynamic material library, a copywriting generation strategy matching model is constructed. Based on the target customer profile tags, a language style matching strategy is adopted, including tone intensity, vocabulary preferences and sentence structure, and differentiated style templates are preset for different age groups and consumption levels. Match copy length, information density, and presentation format based on marketing scenario type and communication channel attributes; By combining core marketing appeal tags with value delivery strategies, we can identify the core selling points and emotional value highlighted in the copywriting, and integrate the matched style, form, and value delivery strategies into a personalized strategy solution.

[0024] Specifically, based on the marketing demand list and dynamic material library, an initial copywriting generation strategy matching model is established; the five dimensions of product, target audience, scenario, appeal, and channel in the demand list are associated with the model feature library, and the tag attributes and historical usage effect data of each material in the material knowledge base are also accessed to construct an associated dataset of "demand tags - material features - strategy rules"; the model is pre-trained using historical high-quality copywriting generation cases to form an initial strategy matching rule library; Driven by the target customer profile tags, we construct a multi-dimensional language style adaptation system; by combining tags such as customer age group, consumption level, and behavioral preferences, we match the corresponding tone intensity, vocabulary preferences and sentence structure; at the same time, we adjust the style bias in combination with the core marketing needs, strengthen the use of words with a sense of urgency for promotional needs, and strengthen the use of words with emotional resonance for brand needs, thus generating precise language style strategy details. Based on the marketing scenario type and communication channel attributes, a copywriting form parameter system is constructed to clarify core parameters such as copy length, information density, structural layout, and opening format; differentiated parameter thresholds are set for different channel characteristics; and the form strategy is adjusted in combination with the time characteristics of the marketing scenario, such as adding holiday-themed introductory sentences for holiday promotion scenarios and strengthening the product differentiation introduction structure for new product launch scenarios, thereby generating form strategy solutions that are adapted to channels and scenarios. Guided by core marketing appeal tags and combined with product attribute tags, the core value delivery direction of the copy is clarified; through the demand-value mapping rule, the appeal of "sales increase" is matched with the strategy of "prioritizing benefits + highlighting discounts", the appeal of "brand exposure" is matched with the strategy of "conveying brand concept + creating emotional resonance", and the appeal of "user reactivation" is matched with the strategy of "exclusive benefits + emotional awakening"; at the same time, high-performance materials of the same type of appeal in the dynamic material library are referenced to extract the core logic of value delivery and supplement it into the strategy.

[0025] See Figure 5 As shown, based on a personalized generation strategy, a core semantic framework for the copy is constructed according to the value delivery strategy. Based on style and form strategies, copy fragments with different sentence structures are generated. The generated copy fragments are then calibrated for emotional tone. Through logical integration of the copy fragments, initial drafts of copy with different emphases are obtained, specifically including: Based on the personalized generation strategy, semantic construction deconstructs the core of value delivery and combines it with related materials in the material library to build a three-level semantic framework of core selling points, supporting arguments, and emotional resonance points. Sentence generation uses style and form strategies to call up vocabulary from basic materials, fill in the content of a three-level semantic framework, and generate multiple differentiated sentence fragments. Emotional optimization calibrates the emotional tone of a segment by matching emotional vocabulary and adjusting modal particles. The fragments were integrated according to semantic logic association rules to form three initial drafts with different focuses, namely product functions, emotional value, and benefits.

[0026] Specifically, the semantic construction module, based on the core requirements of value delivery in the strategy, calls upon the core selling points of the product, the pain points of customer needs, and the scenario-adaptive materials associated with the dynamic material library; through semantic association analysis, it sorts out the logical hierarchy of core information and constructs a basic semantic framework of "scenario introduction - core value output - benefit reinforcement - action guidance", clarifying the core information nodes and expression priorities of each level; the framework structure is adjusted for different marketing needs, and corresponding material retrieval tags are matched for each information node, forming a semantic framework blueprint with material matching guidance; The sentence generation module calls suitable sentence templates from the basic material module based on the language style and copywriting requirements in the strategy. Combining the information node requirements of the semantic framework blueprint, it uses the material retrieval engine to match materials with high tag fit and good historical usage performance to accurately fill the sentence templates. It generates 3-5 different sentence expression fragments for the same information node. For example, core selling points can generate differentiated fragments such as "functional expression + effect expression + comparison expression". At the same time, it strictly follows the requirements of copywriting length, information density and other parameters to adapt and adjust the length of the filled sentence fragments. The sentiment optimization module detects the sentiment intensity of generated sentence fragments based on the sentiment tone threshold set by the strategy. It calibrates the sentiment orientation by replacing sentiment words, adding or removing modal particles, and adjusting sentence rhythm. For example, it adds trendy modal particles to a lively style and weakens modifying words to a calm style. Then, it connects and integrates the sentence fragments according to the logical main line of the semantic framework blueprint, checks the fluency of sentences through contextual semantic similarity analysis, and adds transition sentences at logical breaks. To meet the needs of multiple versions, it adjusts the expression focus of each core information node and generates at least three draft versions.

[0027] See Figure 6 As shown, a copywriting optimization evaluation system is constructed based on compliance with changes, marketing adaptability, and communication potential. The initial draft of the copy is optimized in a targeted manner and quantitatively scored. If it fails to meet the standards, the generation strategy is adjusted until a satisfactory optimized copy is obtained. Specifically, the optimized copy includes: Construct a three-tiered evaluation system that includes compliance, marketing suitability, and dissemination potential. The compliance indicator is based on a pre-set list of prohibited words and rules. The marketing suitability indicator sets a threshold for tag matching and a standard for the accuracy of conveying core selling points. The dissemination potential indicator establishes a readability threshold and attractive keywords. Based on the quantitative scores output by the evaluation system, the initial draft of the copy is optimized in a targeted manner, including replacing prohibited words, strengthening core information, and adjusting the language style; If the optimized copy still has items that do not meet the scoring criteria, the generation strategy will be readjusted until optimized copy that meets the evaluation criteria is generated.

[0028] Specifically, multiple draft versions of the copy are input into the evaluation system. Compliance verification uses a dual mechanism of keyword matching and semantic understanding to screen for non-compliant expressions and mark the type and location of violations. Marketing suitability scoring compares the copy's tag matching degree with the structured requirements list to quantify the accuracy of core information delivery and identify issues such as missing selling points and mismatched target audience language. Communication potential scoring combines a database of historical high-quality copy to analyze readability, attractiveness, and other indicators through text feature comparison and analysis, identifying defects such as bland language and logical gaps. Finally, the system outputs a comprehensive score and individual indicator scores for each version of the copy. Based on the issue list and optimization priorities, tiered optimization will be implemented. Compliance issues will be rectified first, using a dual strategy of "replacing non-compliant words with semantically similar words" to replace them. The replacement word library will retrieve compliant words from the dynamic material knowledge base to ensure that the meaning remains unchanged after modification. Marketing adaptability issues will be optimized by returning to a personalized generation strategy, supplementing missing core selling point information, adjusting the language style to match customer preferences, and enhancing the sense of scenario immersion. Issues related to communication potential will be optimized through sentence reconstruction, strengthening emotional vocabulary, and highlighting memorable information. For example, long sentences will be broken down to improve readability, and questions or benefits will be added at the beginning to enhance attractiveness.

[0029] See Figure 7 As shown, an effectiveness prediction model is built based on historical marketing copy dissemination data to predict the dissemination effect of the copy on various channels, and differentiated adjustments are made to the optimized copy to generate customized versions adapted to different dissemination channels. Specifically, this includes: Call the effect prediction model trained based on historical marketing copy communication data, and import the optimized copy and structured requirement list; The model breaks down and optimizes the core information, language style, and emotional orientation of copywriting, and predicts key performance indicators such as exposure, click-through rate, and conversion potential under different communication channels. Based on the predicted results, targeted adjustments were made to the optimized copy to generate customized versions suitable for different communication channels. These included streamlining the text to within 30 characters and adding interactive prompts for social media channels, strengthening the density of core keywords and highlighting benefits for search engine marketing channels, and adding a scenario-based narrative opening for long video channels.

[0030] Specifically, the formula for predicting the effectiveness of channel communication is: ; in, The predicted click-through rate of version t of the copy on channel d. For model bias terms, Let be the weight coefficient of the i-th feature. Let t be the i-th feature value of the t-th version of the copy on the d-th channel. The total number of features involved in the prediction; The same core copy is used across different channels to maintain consistency in core selling points and brand tone, with key adjustments made to language expression, information presentation structure, and length: For short video platforms, the copy is streamlined to under 50 characters, using a structure of "hot keywords / question opening + core benefits + action instructions" to enhance conversational expression; for social media, scenario-based narrative segments are added, interactive guiding statements are supplemented, and text layout is adapted to the combination of text and images; for search engines, the density and position of core keywords are optimized, highly relevant keywords are placed at the beginning, and explanations of core product parameters are added; for e-commerce platforms, a structure of "layered expression of core selling points + solutions to user pain points + highlighting promotional information" is adopted to enhance data-driven and scenario-based descriptions.

[0031] See Figure 8 As shown, the generated customized copy for each channel is integrated and output, the actual dissemination data after the copy is launched is collected in real time, and compared with the predicted effect to generate effect difference results and update the effect prediction model parameters. Specifically, this includes: Establish a full lifecycle tracking mechanism for copywriting, connect to data interfaces of various communication channels, collect actual dissemination data such as exposure, click-through rate, and conversion rate after copywriting is launched in real time, and generate results on performance differences by comparing them with predicted performance indicators. Based on the discrepancies and historical feedback data, the effect prediction model was updated, the parameters of the evaluation system were optimized, and the triggering conditions for the update mechanism of the material library were simultaneously optimized.

[0032] Specifically, for the dynamic marketing material library, materials with poor actual performance are eliminated, the recommendation weight of high-conversion materials is increased, and high-quality materials favored by users are added; for the personalized generation strategy matching model, the strategy parameters of different demand tag combinations are adjusted, and the matching logic of language style and copywriting format is optimized; for the effect prediction model, the latest delivery data is incorporated to fine-tune the model, correct prediction deviations, and improve prediction accuracy; an iteration cycle management mechanism is established, with regular iterations executed monthly and emergency iterations for high-deviation, high-traffic copywriting triggered weekly; after each iteration, an optimization log is generated to record the iteration content, data support, and optimization effects, forming a closed-loop iteration system of "generation-delivery-feedback-optimization" to continuously improve the quality and efficiency of automated copywriting generation.

[0033] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0034] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0035] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for automatically generating digital marketing advertising copy, characterized in that, include: Receive digital marketing demand information, parse unstructured text through natural language processing, extract keywords and semantic relationships, match industry standard tags to generate a demand matrix, and output a structured demand list after rationality verification; Based on the demand matrix, a dynamic material library is built, which includes basic materials, industry-specific materials, and real-time hot topics. A material update mechanism is established to update the knowledge base materials according to industry dynamics, seasonal changes, and the life cycle of hot topics. Based on a structured list of requirements and a dynamic material library, the length, information density, and presentation format of the copy are matched according to the type of marketing scenario and the communication channel. The value delivery strategy is matched in combination with marketing appeal tags, and the matched style, format, and value delivery strategy are integrated into a personalized generation strategy solution. Based on a personalized generation strategy, a core semantic framework for copywriting is constructed according to a value delivery strategy. Based on style and form strategies, copywriting fragments with different sentence structures are generated. The generated copywriting fragments are then calibrated in terms of emotional tone. By logically integrating the copywriting fragments, initial drafts of copywriting with different focuses are obtained. Build a copywriting optimization evaluation system that considers compliance with changes, marketing adaptability, and communication potential. Optimize the initial drafts of the copy in a targeted manner and score them quantitatively. If the copy does not meet the standards, adjust the generation strategy until the optimized copy meets the standards. Based on historical marketing copy dissemination data, an effect prediction model is built to predict the dissemination effect of the copy on various channels, and the optimized copy is adjusted accordingly to generate customized versions adapted to different dissemination channels. The generated customized copy for each channel is integrated and output, the actual dissemination data after the copy is launched is collected in real time, and compared with the predicted effect to generate the effect difference results and update the effect prediction model parameters.

2. The method for automatically generating digital marketing advertising copy according to claim 1, characterized in that, The process of receiving digital marketing demand information, parsing unstructured text through natural language processing, extracting keywords and semantic relationships, matching industry standard tags to generate a demand matrix, and outputting a structured demand list after rationality verification specifically includes: Receive users' core digital marketing needs information and classify it into structured and unstructured types, including product attribute parameters, target customer profiles, marketing scenario types, core marketing appeals, and communication channel attributes; Natural language processing techniques are used to segment, tag, and perform dependency parsing on unstructured text to extract key terms and semantic relationships. The system invokes a pre-defined industry marketing tagging system to match extracted keywords with product, target audience, scenario, demand, and channel tags, generating a multi-dimensional standardized demand matrix. The standardized demand matrix is ​​validated for rationality, contradictory labels are removed, and missing key demand data is supplemented to output a structured marketing demand list.

3. The method for automatically generating digital marketing advertising copy according to claim 1, characterized in that, The aforementioned construction of a dynamic resource library based on a demand matrix, comprising basic resources, industry-specific resources, and real-time trending resources, and the establishment of a resource update mechanism to update the knowledge base resources according to industry dynamics, seasonal changes, and the life cycle of trending topics, specifically includes: Based on a standardized demand matrix, a knowledge base architecture is built that includes basic materials, industry-specific materials, and real-time trending materials, with preset material classification fields and association rules for each module. The basic materials include general marketing script templates and common product description terms, which are categorized and archived according to marketing scenarios and appeal types. Industry-specific materials include marketing copy and product terminology from various industries, which are crawled using web scraping technology. Real-time trending materials include trending elements from mainstream news platforms and social media trending lists. Establish a material update mechanism to automatically update the material library based on industry trends, seasonal changes, and the life cycle of trending topics, and record historical usage data of the materials.

4. The method for automatically generating digital marketing advertising copy according to claim 1, characterized in that, The process of matching copy length, information density, and presentation format based on a structured requirements list and dynamic material library, according to marketing scenario type and communication channel, and combining marketing appeal tags to match value delivery strategies, integrates the matched style, format, and value delivery strategies into a personalized generation strategy solution, specifically including: Based on a structured marketing needs list and a dynamic material library, a copywriting generation strategy matching model is constructed. Based on the target customer profile tags, a language style matching strategy is adopted, including tone intensity, vocabulary preferences and sentence structure, and differentiated style templates are preset for different age groups and consumption levels. Match copy length, information density, and presentation format based on marketing scenario type and communication channel attributes; By combining core marketing appeal tags with value delivery strategies, we can identify the core selling points and emotional value highlighted in the copywriting, and integrate the matched style, form, and value delivery strategies into a personalized strategy solution.

5. The method for automatically generating digital marketing advertising copy according to claim 1, characterized in that, The personalized generation strategy involves constructing a core semantic framework for the copy based on a value delivery strategy, generating copy fragments with different sentence structures according to style and form strategies, calibrating the emotional tone of the generated copy fragments, and obtaining initial drafts of copy with different emphases through logical integration of the copy fragments. Specifically, this includes: Based on the personalized generation strategy, semantic construction deconstructs the core of value delivery and combines it with related materials in the material library to build a three-level semantic framework of core selling points, supporting arguments, and emotional resonance points. Sentence generation uses style and form strategies to call up vocabulary from basic materials, fill in the content of a three-level semantic framework, and generate multiple differentiated sentence fragments. Emotional optimization calibrates the emotional tone of a segment by matching emotional vocabulary and adjusting modal particles. The fragments were integrated according to semantic logic association rules to form three initial drafts with different focuses, namely product functions, emotional value, and benefits.

6. The method for automatically generating digital marketing advertising copy according to claim 1, characterized in that, The aforementioned copywriting optimization evaluation system, which assesses compliance with changes, marketing adaptability, and communication potential, involves targeted optimization of initial drafts of copy, quantifying their scores, and adjusting the generation strategy if the drafts fail to meet the standards until a satisfactory optimized copy is obtained. Specifically, this includes: Construct a three-tiered evaluation system that includes compliance, marketing suitability, and dissemination potential. The compliance indicator is based on a pre-set list of prohibited words and rules. The marketing suitability indicator sets a threshold for tag matching and a standard for the accuracy of conveying core selling points. The dissemination potential indicator establishes a readability threshold and attractive keywords. Based on the quantitative scores output by the evaluation system, the initial draft of the copy is optimized in a targeted manner, including replacing prohibited words, strengthening core information, and adjusting the language style; If the optimized copy still has items that do not meet the scoring criteria, the generation strategy will be readjusted until optimized copy that meets the evaluation criteria is generated.

7. The method for automatically generating digital marketing advertising copy according to claim 1, characterized in that, The aforementioned method of building an effectiveness prediction model based on historical marketing copy dissemination data to predict the dissemination effect of copy across various channels, making differentiated adjustments to optimized copy, and generating customized versions adapted to different dissemination channels specifically includes: Call the effect prediction model trained based on historical marketing copy communication data, and import the optimized copy and structured requirement list; The model breaks down and optimizes the core information, language style, and emotional orientation of copywriting, and predicts key performance indicators such as exposure, click-through rate, and conversion potential under different communication channels. Based on the predicted results, targeted adjustments were made to the optimized copy to generate customized versions suitable for different communication channels. These included streamlining the text to within 30 characters and adding interactive prompts for social media channels, strengthening the density of core keywords and highlighting benefits for search engine marketing channels, and adding a scenario-based narrative opening for long video channels.

8. The method for automatically generating digital marketing advertising copy according to claim 1, characterized in that, The process of integrating and outputting customized copy for each channel, collecting actual dissemination data after copy placement in real time, comparing it with the predicted results, generating results showing performance differences, and updating the performance prediction model parameters specifically includes: Establish a full lifecycle tracking mechanism for copywriting, connect to data interfaces of various communication channels, collect actual dissemination data such as exposure, click-through rate, and conversion rate after copywriting is launched in real time, and generate results on performance differences by comparing them with predicted performance indicators. Based on the discrepancies and historical feedback data, the effect prediction model was updated, the parameters of the evaluation system were optimized, and the triggering conditions for the update mechanism of the material library were simultaneously optimized.

9. A system for automatically generating digital marketing advertising copy, used to implement the method for automatically generating digital marketing advertising copy as described in any one of claims 1-8, characterized in that, include: Demand parsing module: Uses natural language processing technology to parse unstructured marketing demands input by users, extract keywords and match industry tags, and generate a standardized demand matrix and structured list; Dynamic Material Library Module: Constructs a multi-dimensional material library containing basic scripts, industry-specific content, and real-time hot topics, and updates material data based on industry trends, seasons, and hot topic cycles; Strategy generation module: Based on the requirements list and material library, match the copywriting style, format and value delivery strategy, and output a personalized generated solution; Copy generation module: Based on the strategy, a semantic framework is built to generate multiple draft versions, and emotional calibration and logical integration are performed to output copy with different focuses such as function, emotion, and discounts; Intelligent optimization module: The initial draft is quantitatively scored and optimized through a three-dimensional evaluation system of compliance, adaptability, and dissemination potential. If it fails to meet the standards, the generation strategy is iteratively adjusted until it meets the standards. Channel adaptation module: Utilizes performance prediction models to adjust copywriting style for different channels and generate customized versions; Data feedback module: Collects data in real time and compares it with the prediction results, updates model parameters and material library; Processor: The processor is used to handle the calculation process of each formula and the construction calculation process of each model.

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