Generative engine optimization method and system based on STREAM framework

Abstract

The invention discloses a STREAM framework-based generative engine optimization method and system, and relates to the technical field of GEO generative engine optimizing.The STREAM framework-based generative engine optimization method comprises the steps of collecting content data of a brand on each platform; analyzing the semantic structuring index S of the brand content, and evaluating the semantic structuring degree of the brand content; analyzing the time correlation coefficient T, and evaluating the timeliness of the brand content; analyzing the credible source cross authentication number R, and evaluating the credibility and authority of the brand content; analyzing the user resonance index E, and evaluating the resonance degree of the brand content; analyzing the content consistency score A, and evaluating the consistency of the content in different platforms and modes; and dynamically adjusting the weight of each dimension through a multi-modal search weight dynamic fine tuning algorithm M, calculating a GEO comprehensive score based on the weight of each dimension and the scores of the five dimensions of S, T, R, E and A, and optimizing the brand content according to the GEO comprehensive score. And an operable technical path is provided for the brand to establish cognitive advantages in the generative AI era.

Description

Technical Field

[0001] The present invention relates to the field of generative engine optimization (GEO) technology, and in particular to a STREAM framework-based GEO optimization method and system. Background Art

[0002] With the rapid development and widespread application of large-scale language models (LLMs) such as ChatGPT, DeepSeek, iFlytek, Kimi, and Doubao, generative AI has become a crucial gateway for information acquisition and knowledge dissemination. In this emerging information ecosystem, how brands can be accurately understood, effectively indexed, and prioritized by AI systems has become a new challenge for digital marketing and brand building. Traditional search engine optimization (SEO) techniques primarily target search engine algorithms based on keyword matching and link analysis. Core strategies include keyword optimization, external link building, and website structure optimization. However, these methods have limited effectiveness in the context of generative large-scale AI search, primarily due to the following issues: 1. Keyword density analysis cannot adapt to the semantic understanding mechanisms of large models; 2. External link count assessment cannot meet the multi-dimensional verification requirements of AI systems for information credibility; 3. Traditional website structure optimization fails to consider AI's understanding of content semantic structure; 4. It lacks systematic assessment of content timeliness, user resonance, and cross-platform consistency; and 5. It cannot dynamically adjust optimization strategies for different query scenarios.

[0003] Existing AI optimization technologies mostly focus on prompt engineering, primarily addressing how users can better ask AI systems questions, rather than how brands can be better understood and recommended by AI systems. The market lacks systematic methodologies and technical solutions to guide brands in building cognitive advantages in generative AI environments.

[0004] To this end, the present invention provides a generative engine optimization method and index evaluation system based on the STREAM framework. Summary of the Invention

[0005] (1) Technical problems solved In view of the deficiencies of the prior art, the application provides a generative engine optimization method and system based on a STREAM framework. The application dynamically adjusts the weights of each dimension through a multimodal search weight dynamic fine-tuning algorithm M, calculates a GEO comprehensive score based on the weights of each dimension and scores of five dimensions S, T, R, E and A, realizes precise adaptation of brand content optimization in different scenarios, improves the understanding accuracy, collection efficiency and recommendation probability of brand information in an AI system, provides an operable technical path for a brand to establish a cognitive advantage in the generative AI era, and thus solves the technical problems recorded in the background art.

[0006] (II) Technical solutions To achieve the above object, the application is implemented by the following technical solutions: a generative engine optimization method based on a STREAM framework, comprising the following steps: Collecting public content and data of a brand in each generative engine, i.e., a large model platform; Analyzing a semantic structuring index S of brand content to evaluate the semantic organization and structured degree of the brand content; Analyzing a timeliness factor T to evaluate the timeliness of the content; Analyzing a redundancy of verified sources R to evaluate the credibility and authority of the brand content; Analyzing an engagement weight E to evaluate the matching degree and resonance degree of the brand content and user search; Analyzing an alignment score A to evaluate the consistency and coordination of the content in different platforms and modalities; Dynamically adjusting the weights of each dimension through a multimodal search weight dynamic fine-tuning algorithm M, calculating a GEO comprehensive score based on the weights of each dimension and scores of five dimensions S, T, R, E and A, and optimizing the brand content accordingly.

[0007] Further, an AI agent or a crawler accesses mainstream large model search result pages and their information source pages (such as social / e-commerce / media platforms), automatically captures brand names, product names and related keywords appearing in public pages of social media, news websites, forums, blogs and e-commerce evaluation, accurately extracts titles, texts, authors, publishing times, like / shares / comment numbers and the like, and constructs a brand content library.

[0008] Further, all professional terms in the brand content library are extracted using NLP tools to establish a brand-specific term library, define standard expressions, and calculate the frequency of non-standard expressions outside the term library using regular expressions or NLP matching. Concept clarity = (1 - number of non-standard terms / total number of terms) x 100%.

[0009] Further, the titles, subtitles, lists, and other structured elements in the brand content library are extracted using Python's Beautiful Soup and PDFMiner to calculate the coverage rate, which is the logical hierarchy. Logical hierarchy = number of structured elements / total number of paragraphs x 100%.

[0010] Further, the concept clarity and logical hierarchy are normalized and weighted to analyze the semantic structure index S to evaluate the structured degree of brand content. That is, S = a * concept clarity + (1-a) * logical hierarchy, where a is the semantic structure index analysis weight.

[0011] Further, the page content is regularly scraped, and a content change detection engine is built based on MD5 / SHA-256 hash value comparison technology. Combined with an automated timestamp capture mechanism, a time series database of content update events is established to ultimately output the dynamic update frequency of the content update frequency and determine the brand content update frequency.

[0012] Further, the number of content with explicit time markers and the total number of content in the brand content library are counted to analyze the explicitness of brand content time markers. Time marker explicitness = number of explicit time markers / total number of content.

[0013] Further, the brand content update frequency and time marker explicitness are normalized and weighted to analyze the time correlation coefficient T to evaluate the timeliness of brand content. That is, T = b * 1 / brand content update frequency + (1-b) * time marker explicitness, where b is the time correlation coefficient analysis weight.

[0014] Further, brand content citation records are scraped from academic databases and news aggregation platforms, and authoritative sources are divided into different levels with corresponding weights. The number of citations of brand content in different sources is counted, and the authoritative citation density is calculated, .

[0015] The weights of authoritative sources are as follows: national media S level: 1, provincial media A level: 0.8, local media B level 0.6, and other media C level 0.4.

[0016] Further, based on the number of citations of brand content in different sources, the strength of the trust network is analyzed. Trust network strength = total number of citations / average number of citations of brand content in the same industry.

[0017] Further, the authoritative reference density and trust network strength are normalized and weighted to analyze the cross-certification number R of trusted sources. That is, R = c * authoritative reference density + (1-c) * trust network strength, and c is the analysis weight of the cross-certification number of trusted sources.

[0018] Further, the number of brand content likes, forwards, and comments is obtained, and the brand content interaction strength is analyzed. .

[0019] Further, a brand-specific sentiment dictionary is constructed, such as "innovation" and "reliability" as positive words, and "outdated" and "expensive" as negative words. Based on sentiment analysis API, the number of negative and positive sentiment reaction words in the brand content sub-comment area is analyzed, and the brand content sentiment trigger strength is analyzed. Sentiment trigger strength = positive sentiment reaction word number / (negative sentiment reaction word number + positive sentiment reaction word number).

[0020] Further, the and sentiment trigger strength are normalized and weighted to analyze the user resonance index E. That is, E = d + (1-d) * sentiment trigger strength, and d is the analysis weight of the user resonance index.

[0021] Further, the cosine similarity is used to calculate the text similarity of brand content on different platforms, denoted as cross-platform consistency. Deep learning models such as convolutional neural networks CNN or visual Transformers ViT are used to extract high-level semantic features from images, and natural language processing models are used to extract semantic features from text. The CLIP model is used to map image and text features to the same semantic space to realize text-image cross-modal alignment. Cosine similarity or Euclidean distance measurement methods are used to calculate the similarity between image and text embedding vectors, denoted as multi-modal coordination.

[0022] Further, the and multi-modal coordination are normalized and weighted to analyze the content consistency score A. That is, A = e + (1-e) * multi-modal coordination, and e is the analysis weight of the content consistency score.

[0023] Furthermore, the initial evaluation weights of five dimensions (α, β, γ, δ, ε) are preset, and LDA or NMF is used to extract user query topics and map them to predefined scenarios. If the predefined scenarios are not matched, the initial evaluation weights are output as the GEO comprehensive score weights (α', β', γ', δ', ε'). If the predefined scenarios are matched, the evaluation weights of the five dimensions, namely the semantic structured index S, the time correlation coefficient T, the trusted source cross-certification number R, the user resonance index E, and the content consistency score A, are automatically adjusted according to the query scenario classification and output as the GEO comprehensive score weight (α', β', γ', δ', ε'); and the GEO comprehensive score weights are normalized.

[0024] Furthermore, the scores of the five dimensions were standardized so that the score range of all dimensions was unified to 0-100.

[0025] Furthermore, the final GEO comprehensive score ranges from 0-100, specifically: 90-100 indicates excellent brand content, 80-89 indicates good brand content, 70-79 indicates good brand content, 60-69 indicates average brand content, and below 60 indicates poor brand content.

[0026] A generative engine optimization system based on the STREAM framework, comprising: The data collection module collects the public content and data of brands in various generative engines, namely the large model platform; The STREAM evaluation module includes five evaluation sub-units: S, T, R, E, and A. The S evaluation sub-unit analyzes the semantic structured index S of brand content to assess the degree of semantic structuredness of brand content; the T evaluation sub-unit analyzes the time correlation coefficient T to assess the timeliness of brand content; the R evaluation sub-unit analyzes the trusted source cross-certification number R to assess the credibility and authority of brand content; the E evaluation sub-unit analyzes the user resonance index E to assess the degree of resonance of brand content; and the A evaluation sub-unit analyzes the content consistency score A to assess the consistency of content across different platforms and modalities. M algorithm module, responsible for dynamically adjusting the weights of each dimension; Comprehensive scoring module, which calculates the GEO comprehensive score based on the weight of each dimension and the scores of the five dimensions S, T, R, E, and A; Optimization suggestion module, which generates optimization suggestions based on comprehensive scoring results; Effect monitoring module, monitors optimization effects and provides feedback.

[0027] (3) Beneficial effects The present invention provides a generative engine optimization method and system based on the STREAM framework, which has the following beneficial effects: 1. Collect brand content data on various platforms, analyze the semantic structured index S of brand content, evaluate the semantic structured degree of brand content, convert unstructured content into quantifiable indicators, reveal the logicality and information density of brand content, break through the limitations of traditional word frequency statistics, evaluate content value from the cognitive level, ensure that core information is easy for users to understand and remember, and provide bottom-layer data support for subsequent strategies.

[0028] 2. Analyze the time correlation coefficient T to evaluate the timeliness of brand content, quantify the sensitivity of the brand to market trends, avoid "expired" brand content, promote real-time adjustment of brand content strategy, and enhance the market adaptability of brand content.

[0029] 3. Analyze the number of cross-certified trusted sources R to evaluate the credibility and authority of brand content, improve content credibility, effectively resist false information, establish an authoritative barrier, and provide a traceable evidence chain for brand endorsement.

[0030] 4. Analyze the user resonance index E to evaluate the resonance degree of brand content, quantify the emotional penetration of content, directly link user loyalty, and guide the construction of emotional narrative by the brand.

[0031] 5. Analyze the content consistency score A to evaluate the consistency of content on different platforms and modalities, solve the cognitive confusion caused by "channel fragmentation", and strengthen user mental anchoring.

[0032] 6. Dynamically adjust the weights of each dimension through a multi-modal search weight dynamic tuning algorithm M, calculate the GEO comprehensive score based on the weights of each dimension and the scores of the five dimensions S, T, R, E, and A, realize the precise adaptation of brand content optimization in different scenarios, improve the understanding accuracy, inclusion efficiency and recommendation probability of brand information in the AI system, and provide an operable technical path for building cognitive advantage for brands in the generative AI era. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 The flowchart of the STREAM framework-based generative engine optimization method of the present application; Figure 2 The schematic table of pre-defined scene-GEO comprehensive score weight corresponding to the present application; Figure 3 The structure diagram of the STREAM framework-based generative engine optimization system of the present application. DETAILED DESCRIPTION

[0034] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0035] Referring to Figures 1-2 The present application provides a generative engine optimization method based on a STREAM framework, comprising the following steps: Step one, collect brand content data on each platform, analyze the semantic structuring index (Semantic Structuring Index, S) of brand content, and evaluate the semantic structuring degree of brand content.

[0036] The step one comprises the following steps: Step 101, use a crawler or access mainstream social / e-commerce / media platform API to automatically capture brand names, product names, and related keywords appearing in social media, news websites, forums, blogs, e-commerce reviews, and other public pages, accurately extract titles, texts, authors, publication times, likes / shares / comments, etc., and build a brand content library.

[0037] Step 102, use NLP tools to extract all professional terms from the brand content library, establish a brand-specific term library, define standard expressions, and through regular expressions or NLP matching, count the appearance frequency of expressions outside the term library as concept clarity. Concept clarity=(1-non-standard term quantity / total term quantity)×100%.

[0038] Step 103, use Python's Beautiful Soup and PDFMiner to extract structured elements such as titles, subtitles, and lists from the brand content library, and perform coverage calculation as logical hierarchy, logical hierarchy=structured element quantity / total paragraph number×100%.

[0039] Step 104, normalize and weight the concept clarity and logical hierarchy to analyze the semantic structuring index S and evaluate the structuring degree of brand content. That is, S=a*concept clarity+(1-a)*logical hierarchy, a is the analysis weight of the semantic structuring index.

[0040] In use, the contents in steps 101 to 104 are combined: Collect the brand's content data on each platform, analyze the semantic structured index S of the brand's content, evaluate the semantic structured degree of the brand's content, convert unstructured content into quantifiable indicators, reveal the logicality and information density of the brand's content, break through the limitations of traditional word frequency statistics, evaluate the content value from the cognitive level, ensure that the core information is easy for users to understand and remember, and provide bottom-layer data support for subsequent strategies.

[0041] Step two, analyze the timeliness factor (T) to evaluate the timeliness of the brand's content.

[0042] The second step includes the following contents: Step 201, regularly capture page content, build a content change detection engine based on MD5 / SHA-256 hash value comparison technology, combine an automatic timestamp capture mechanism, establish a time series database of content update events, and finally output the content update frequency of dynamic updates to determine the brand content update frequency.

[0043] Step 202, count the number of contents with explicit time markers and the total number of contents in the brand content library, analyze the explicitness of the brand content time markers, and the explicitness of the time markers = the number of contents with explicit time markers / total contents.

[0044] Step 203, analyze the timeliness factor T by summing the brand content update frequency and the explicitness of the time markers, evaluate the timeliness of the brand's content. That is, T = b*1 / brand content update frequency + (1-b)*time marker explicitness, b is the analysis weight of the timeliness factor.

[0045] In use, combined with the contents in steps 201 to 203: Analyze the timeliness factor T to evaluate the timeliness of the brand's content, quantify the sensitivity of the brand to market trends, avoid "expiration" of the brand's content, promote the brand to adjust the content strategy in real time, and enhance the market adaptability of the brand's content.

[0046] Step three, analyze the redundancy of verified sources (R) to evaluate the credibility and authority of the brand's content.

[0047] The third step includes the following contents: Step 301, capture brand content citation records through academic databases and news aggregation platforms, divide authoritative sources into different levels and assign corresponding weights, count the number of citations of brand content in different sources, and calculate the authoritative citation density, .

[0048] The weight of the authoritative source is as follows: national media S level: 1, provincial media A level: 0.8, local media B level 0.6, and the rest C level 0.4.

[0049] Step 302, analyze the trust network strength based on the number of references of brand content in different sources, trust network strength = total number of references / average number of references of brand content in the same industry.

[0050] Step 303, sum the authoritative reference density and trust network strength to analyze the cross-certification number R of trusted sources. That is, R = c * authoritative reference density + (1-c) * trust network strength, c is the analysis weight of cross-certification number of trusted sources.

[0051] In use, combine the contents in steps 301 to 303: Analyze the cross-certification number R of trusted sources to evaluate the credibility and authority of brand content, improve content credibility, effectively resist false information, establish an authoritative barrier, and provide a traceable evidence chain for brand endorsement.

[0052] Step four, analyze the user engagement index (Engagement Weight, E) to evaluate the engagement level of brand content.

[0053] The fourth step includes the following contents: Step 401, obtain the number of likes, forwards, and comments of brand content, and analyze the interaction strength of brand content, .

[0054] Step 402, build a brand-specific sentiment dictionary, such as "innovation" and "reliability" as positive words, and "outdated" and "expensive" as negative words. Based on sentiment analysis API, analyze the number of negative and positive emotional response words in the comment area of brand content, analyze the emotional trigger strength of brand content, and emotional trigger strength = positive emotional response word number / (negative emotional response word number + positive emotional response word number).

[0055] Step 403, sum the and emotional trigger strength to analyze the user engagement index E. That is, E = d + (1-d) * emotional trigger strength, d is the analysis weight of user engagement index.

[0056] In use, combine the contents in steps 401 to 403: Analyze the user engagement index E to evaluate the engagement level of brand content, quantify the emotional penetration of content, directly link user loyalty, and guide brand to build emotional narrative.

[0057] Step five, analyze the alignment score (A) to evaluate the consistency of the content in different platforms and modalities. The step five includes the following content: Step 501, calculate the text similarity of different platform brand content using cosine similarity, denoted as cross-platform consistency. Use deep learning models such as convolutional neural networks (CNN) or visual Transformers (ViT) to extract high-level semantic features from images, and use natural language processing models to extract semantic features from text. Use the CLIP model to map image and text features to the same semantic space to achieve text-image cross-modal alignment. Use cosine similarity or Euclidean distance measurement methods to calculate the similarity between image and text embedding vectors, denoted as multimodal coordination.

[0058] Step 502, calculate the semantic structure index S of the content using the LDA or NMF model. And the multimodal coordination is normalized and weighted to analyze the content alignment score A. That is, A = e * S + (1-e) * multimodal coordination, e is the content alignment score analysis weight.

[0059] In use, combined with the content in steps 501 and 502: Analyze the content alignment score A to evaluate the consistency of the content in different platforms and modalities, solve the cognitive confusion caused by "channel fragmentation", and strengthen the user's mental anchor.

[0060] Step six, dynamically adjust the weights of each dimension through the multimodal search weight dynamic fine-tuning algorithm (M), calculate the GEO comprehensive score based on the weights of each dimension and the scores of S, T, R, E, A five dimensions, and optimize the brand content accordingly. The step six includes the following content: Step 601, preset the initial evaluation weights of the five dimensions (α, β, γ, δ, ε), extract the user query topic using LDA or NMF, map it to the predefined scene, and output the initial evaluation weights as the GEO comprehensive score weights (α', β', γ', δ', ε') if it does not match the predefined scene. If it matches the predefined scene, automatically adjust the evaluation weights of the semantic structure index S, the time correlation coefficient T, the number of cross-certified trusted sources R, the user resonance index E, and the content alignment score A five dimensions according to the query scene classification, and then output as the GEO comprehensive score weights (α', β', γ', δ', ε').

[0061] ​To ensure that the sum of the weights is 1, normalization is performed: α'' = α' / (α' + β' + γ' + δ' + ε') Other weights β'', γ'', δ'', ε'' are also similarly normalized.

[0062] Step 602, based on brand content semantic structure index S, time correlation coefficient T, cross authentication number of trusted sources R, user resonance index E, content consistency score A and GEO comprehensive score weight calculation GEO comprehensive score, and optimize brand content accordingly.

[0063] To ensure the comparability of the scores of each dimension, the scores of the five dimensions are standardized: S_norm = ((S -S_min) / (S_max - S_min))*100 Other dimensions T, R, E, A are also similarly standardized, so that the score range of all dimensions is unified to 0-100.

[0064] The final GEO comprehensive score ranges from 0 to 100, specifically: 90-100 indicates that the brand content is excellent and has a significant competitive advantage in the generative AI environment, 80-89 indicates that the brand content is excellent and has good AI visibility and recommendation probability, 70-79 indicates that the brand content is good and basically meets the AI recommendation requirements, but has room for improvement, 60-69: indicates that the brand content is general and needs to be optimized, and below 60, indicates that the brand content is poor and difficult to obtain effective recommendation in the AI environment.

[0065] In use, in combination with the content in steps 601 and 602: The multi-modal search weight dynamic fine-tuning algorithm M dynamically adjusts the weights of each dimension, and calculates the GEO comprehensive score based on the weights of each dimension and the scores of the five dimensions S, T, R, E, A, which realizes the precise adaptation of brand content optimization in different scenarios, improves the understanding accuracy, collection efficiency and recommendation probability of brand information in the AI system, and provides an operable technical path for brands to establish cognitive advantage in the generative AI era.

[0066] Please refer to Figure 3 The present application provides a kind of generative engine optimization system based on STREAM framework, comprising: Data acquisition module, acquires the public content and data of brand in each generative engine, i.e. large model platform; The STREAM evaluation module includes five-dimension evaluation subunits of S, T, R, E and A. The S evaluation subunit analyzes a semantic structural index S of brand content to evaluate the semantic structural degree of the brand content. The T evaluation subunit analyzes a time correlation coefficient T to evaluate the timeliness of the brand content. The R evaluation subunit analyzes a cross-authentication number R of a reliable source to evaluate the reliability and authority of the brand content. The E evaluation subunit analyzes a user resonance index E to evaluate the resonance degree of the brand content. The A evaluation subunit analyzes a content consistency score A to evaluate the consistency of the content in different platforms and modes.

[0067] The M algorithm module is responsible for dynamically adjusting the weight of each dimension. The comprehensive score module calculates a GEO comprehensive score based on the weight of each dimension and the five-dimension scores of S, T, R, E and A.

[0068] The optimization suggestion module generates optimization suggestions based on the comprehensive score results.

[0069] The effect monitoring module monitors the optimization effect and feeds back.

[0070] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized in the form of a computer program product wholly or partially. Those skilled in the art can realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software depends on the specific application and design constraints of the technical solutions.

[0071] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiments according to actual needs.

[0072] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A generative engine optimization method based on the STREAM framework, characterized by: The steps include: Collect the brand's public content and data on various generative engines, i.e., large model platforms; Analyze the Semantic Structuring Index (S) of brand content to assess the semantic organization and structuring of brand content; Analyze the timeliness factor (T) to evaluate the timeliness of the content; Analyze the Redundancy of Verified Sources (R) to assess the credibility and authority of brand content; Analyze the user resonance index Engagement Weight (E) to assess the match and resonance between brand content and user searches; Analyze the content alignment score (A), which evaluates the consistency and coordination of content across different platforms and modalities; Through the Multimodal Search Weight Dynamic Fine-Tuning algorithm (M for short), the weights of each dimension are dynamically adjusted, and the GEO comprehensive score is calculated based on the weights of each dimension and the scores of the five dimensions S, T, R, E, and A, and brand content is optimized accordingly.

2. The generative engine optimization method based on the STREAM framework according to claim 1, characterized in that: The semantic structuring index S is analyzed by normalizing and weighting the conceptual clarity and logical hierarchy and evaluating the degree of structuring of brand content; that is, S = a*conceptual clarity + (1-a)*logical hierarchy, where a is the analysis weight of the semantic structuring index.

3. The generative engine optimization method based on the STREAM framework according to claim 1, characterized in that: The brand content update frequency and time tag clarity are normalized and weighted to analyze the time correlation coefficient T to evaluate the timeliness of the brand content; that is, T=b*1 / brand content update frequency+(1-b)*time tag clarity, where b is the time correlation coefficient analysis weight.

4. The generative engine optimization method based on the STREAM framework according to claim 1, characterized in that: The authority citation density and trust network strength are normalized and weighted to analyze the trusted source cross-certification number R; that is, R = c * authority citation density + (1-c) * trust network strength, where c is the analysis weight of the trusted source cross-certification number.

5. The generative engine optimization method based on the STREAM framework according to claim 1, characterized in that: Will The user resonance index E is analyzed by normalizing and weighting the emotional trigger intensity; that is, E=d* + (1-d) * emotional trigger intensity, where d is the user resonance index analysis weight.

6. The generative engine optimization method based on the STREAM framework according to claim 1, characterized in that: Will and the normalized weighted sum analysis of multimodal coordination to obtain the content consistency score A; That is A=e* + (1-e)*multimodal coordination, where e is the weight of the content consistency score analysis.

7. The generative engine optimization method based on the STREAM framework according to claim 1, characterized in that: Initial evaluation weights (α, β, γ, δ, ε) of five dimensions are preset, and user query topics are extracted using LDA or NMF and mapped to predefined scenarios. If no predefined scenarios are matched, the initial evaluation weights are output as GEO comprehensive score weights (α', β', γ', δ', ε'). If a predefined scenario is matched, the evaluation weights of the five dimensions (semantic structured index S, time correlation coefficient T, trusted source cross-certification number R, user resonance index E, content consistency score A) are automatically adjusted according to the query scenario classification and output as GEO comprehensive score weights (α', β', γ', δ', ε'); and the GEO comprehensive score weights are normalized.

8. The generative engine optimization method based on the STREAM framework according to claim 1, characterized in that: The scores of the five dimensions are standardized and indexed so that the score range of all dimensions is unified to 0-100.

9. The generative engine optimization method based on the STREAM framework according to claim 1, characterized in that: The final GEO index score ranges from 0-100, specifically: 90-100 indicates excellent brand content, 80-89 indicates good brand content, 70-79 indicates good brand content, 60-69 indicates average brand content, and below 60 indicates poor brand content.

10. A generative engine optimization system based on the STREAM framework, used to implement the method according to any one of claims 1 to 9, characterized in that: include: The data collection module is used to collect the public content and data of brands in various generative engines, namely large model platforms; The STREAM evaluation module includes five evaluation sub-units: S, T, R, E, and A. The S evaluation sub-unit analyzes the semantic structured index S of brand content to assess the degree of semantic structuredness of brand content; the T evaluation sub-unit analyzes the time correlation coefficient T to assess the timeliness of brand content; the R evaluation sub-unit analyzes the trusted source cross-certification number R to assess the credibility and authority of brand content; the E evaluation sub-unit analyzes the user resonance index E to assess the degree of resonance of brand content; and the A evaluation sub-unit analyzes the content consistency score A to assess the consistency of content across different platforms and modalities. M algorithm module, responsible for dynamically adjusting the weights of each dimension; Comprehensive scoring module, which calculates the GEO comprehensive score based on the weight of each dimension and the scores of the five dimensions S, T, R, E, and A; Optimization suggestion module, which generates optimization suggestions based on comprehensive scoring results; Effect monitoring module, monitors optimization effects and provides feedback.

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