AI-driven digital publication content and online derivative resource performance prediction system

Abstract

The invention discloses an AI-driven digital publication content and online derivative resource performance prediction system, which belongs to the technical field of digital publication, and comprises a multi-modal data acquisition layer for capturing publication content metadata, user reading tracks and social media UGC in real time through an API (Application Program Interface); the feature engineering engine comprises a text feature engine, a visual feature engine and a time sequence feature engine; the prediction model cluster comprises a basic prediction module, a generation enhancement module and a dynamic feedback system; and a content understanding and semantic analysis module. According to the method, a multi-modal data source and a deep semantic analysis technology are integrated, a full-dimensional analysis framework covering text, vision and time sequence features is constructed, internal association between content themes and audience emotions can be accurately captured based on entity relationship mining of a knowledge graph and a cross-modal alignment mechanism, and the market demand model of dynamic evolution is combined to obtain a new market demand model. And quantitative evaluation from content quality to market potential is realized.

Description

Technical Field

[0001] The present invention relates to the field of digital publishing technology, and in particular to an AI-driven digital publishing content and online derivative resource performance prediction system. Background Art

[0002] The digital publishing industry has long faced the pain points of highly subjective content value assessments and a lack of data support for derivative development decisions. Traditional methods rely on manual experience and judgment, making it difficult to capture market signals in multimodal data in real time. In addition, the patterns of cross-platform dissemination are complex, resulting in long content development cycles and high trial-and-error costs. In existing technologies, natural language processing models are mostly limited to single text analysis, the association modeling between visual content and user behavior data is insufficient, and time series prediction models lack dynamic adaptability to the content lifecycle. At the same time, the creative generation of derivatives is poorly matched to market demand, and copyright protection measures lag behind the speed of digital content dissemination. Although the field of deep learning has made progress in cross-modal learning and generative models, it has not yet formed a complete technology chain covering data collection, semantic understanding, dynamic prediction, and virtual-reality verification; therefore, we propose an AI-driven digital publishing content and online derivative resource performance prediction system to solve this problem. Summary of the Invention

[0003] The purpose of the present invention is to provide an AI-driven digital publishing content and online derivative resource performance prediction system to solve the problems raised in the above background technology.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] An AI-driven performance prediction system for digital publishing content and online derivative resources, comprising:

[0006] Multimodal data collection layer: Real-time capture of publication content metadata, user reading history, social media UGC, and search engine trends through API interfaces;

[0007] Feature engineering engine, including text feature engine, visual feature engine and time series feature engine;

[0008] A prediction model cluster, comprising a basic prediction module, a generation enhancement module, and a dynamic feedback system. The basic prediction module models cross-modal associations based on the Transformer's multi-head attention mechanism, while the generation enhancement module fine-tunes the Stable Diffusion algorithm to generate derivative resource prototypes. The dynamic feedback system receives market data in real time to update model parameters.

[0009] The content understanding and semantic parsing module analyzes the deep semantics and emotional associations of multimodal data to build a structured cognitive framework to support content value assessment;

[0010] Intelligent decision-making and resource scheduling modules enable dynamic resource allocation and copyright protection based on prediction results, optimizing management decisions throughout the entire life cycle of published content.

[0011] The virtual-reality fusion simulation module builds a virtual market environment to simulate content dissemination paths, verify the feasibility of derivative strategies, and predict potential risks;

[0012] The value chain extension module quantitatively evaluates the cross-domain development potential of content, guides the development path of derivatives, and realizes the full-chain transformation of cultural value into commercial value.

[0013] Preferably, the text feature engine uses RoBERTa-Large to extract semantic vectors and construct a narrative feature matrix with 200+ dimensions. The visual feature engine uses the CLIP model to analyze visual elements such as cover design and illustration style. The temporal feature engine uses LSTM to capture the life cycle curve of content popularity.

[0014] Preferably, the content understanding and semantic parsing module includes:

[0015] The semantic depth analysis unit uses knowledge graph technology to analyze entity relationships in the text, construct a three-dimensional semantic network of author-subject-reader group, receive text features output by the feature engineering engine, and provide structured semantic data to the prediction model;

[0016] The sentiment tendency discrimination unit detects the sentiment polarity of user comments through a pre-trained language model, establishes a time series of sentiment fluctuations, and interacts with the time series feature engine to jointly construct a content popularity decay model;

[0017] The cross-modal alignment unit calculates the semantic consistency score between the cover visual elements and the text theme, generates a cross-modal alignment matrix, provides image-text matching indicators to the generation enhancement module, and guides the design of derivatives.

[0018] Preferably, the intelligent decision-making and resource scheduling module includes:

[0019] The resource priority assessment unit automatically divides resources into S / A / B / C levels based on the content value prediction results, generates resource delivery strategies, receives the output of the prediction model cluster, and pushes resource allocation plans to publishing organizations;

[0020] The dynamic pricing calculation unit combines market demand forecasts and historical price curves to build a simulation pricing model, share user behavior data with the feature engineering engine, and provide decision support for derivatives pricing;

[0021] The copyright intelligent management unit automatically detects content copyright features, generates digital watermark embedding solutions and infringement monitoring rules, and works in conjunction with the multimodal data collection layer to scan online infringing content in real time.

[0022] Preferably, the virtual-reality fusion simulation module includes:

[0023] The digital twin construction unit creates a virtual publishing market environment, simulates the content dissemination path under different marketing strategies, receives the initial parameters output by the prediction model, and feeds back the simulation results to optimize the model;

[0024] The risk stress testing unit injects extreme market volatility parameters to assess the content's risk resistance and the effectiveness of emergency plans, forming a closed loop with the dynamic feedback system to improve the model's robustness;

[0025] The virtual user behavior simulation unit generates virtual reader groups with different demographic characteristics, tests content acceptance, and provides test samples for derivative product design to generate enhancement modules.

[0026] Preferably, the value chain extension module includes:

[0027] The IP value assessment unit quantitatively analyzes the derivative development potential of content IP, generates a fitness score, and works in conjunction with the generation enhancement module to screen high-value derivative development directions;

[0028] The cross-platform communication analysis unit builds a multi-platform communication dynamics model, predicts the fission index of content in channels such as short videos and social media, and provides channel selection recommendations for the resource scheduling module;

[0029] The life cycle management unit tracks the entire cycle data of content from listing to delisting, automatically generates reprint / reproduction decision recommendations, and pushes content decline warning signals to the dynamic feedback system.

[0030] Preferably, the multimodal data acquisition layer is connected to a data governance enhancement layer and a model interpretability module, and the data governance enhancement layer includes:

[0031] Heterogeneous data cleaning unit deploys an adaptive data cleaning pipeline, develops an adversarial training denoising model for noisy user review data, establishes a multi-source data confidence assessment system, and dynamically allocates data weights;

[0032] The privacy compliance engine integrates a differential privacy mechanism, performs k-anonymization on user reading trajectories, develops a copyright data desensitization algorithm, and automatically blurs sensitive text fragments;

[0033] Preferably, the model interpretability module includes:

[0034] The prediction traceability visualization unit generates a heat attribution map to display the key feature dimensions that affect market predictions, builds a decision tree mapping model, and explains the derivation path of the success probability of derivatives;

[0035] The bias detection and correction unit uses a fairness constraint algorithm to eliminate potential biases such as author gender and subject type, and develops content diversity evaluation indicators to prevent homogeneous recommendations.

[0036] The beneficial effects of the present invention are:

[0037] 1. In the present invention, the AI-driven digital publishing content and online derivative resource performance prediction system integrates multimodal data sources and deep semantic analysis technology to build a full-dimensional analysis framework covering text, visual, and temporal features. Based on the entity relationship mining and cross-modal alignment mechanism of the knowledge graph, it can accurately capture the inherent relationship between content themes and audience emotions. Combined with the dynamically evolving market demand model, it can achieve quantitative evaluation from content quality to market potential. This technical approach that integrates cognitive computing and predictive analysis significantly enhances the objectivity of published content value assessment, provides reliable data support for topic planning and derivative development, and reduces the risk of market misjudgment.

[0038] 2. In the present invention, the AI-driven digital publishing content and online derivative resource performance prediction system described above has an intelligent decision-making module that dynamically optimizes resource allocation strategies through machine learning algorithms, combines content value predictions with market demand fluctuations, and establishes a flexible resource allocation mechanism. The integrated copyright intelligent management unit uses digital watermarking and infringement monitoring technology to build a protection system throughout the content lifecycle. This architecture, which deeply integrates commercial value mining with intellectual property protection, not only improves the monetization efficiency of high-quality content, but also effectively curbs copyright abuse in the digital content ecosystem, forming a virtuous cycle of sustainable development.

[0039] 3. In the present invention, the AI-driven digital publishing content and online derivative resource performance prediction system described above uses a generation enhancement module combined with cross-modal semantic matching technology to break through the content boundaries of traditional derivative product design. It accelerates creative iteration through AI-generated prototypes and constructs a virtual market sandbox in a virtual-reality fusion simulation environment. This system can simulate dissemination paths and user feedback under different marketing strategies, and identify potential market risks in derivative product development in advance. This closed-loop system, which combines creative generation with rigorous verification, significantly shortens the cycle from creative conception to market verification, while reducing trial and error costs, providing scientific decision-making support for the diversified development of cultural IP.

[0040] 4. In the present invention, the AI-driven digital publishing content and online derivative resource performance prediction system, described in its value chain extension module, quantitatively evaluates the cross-domain adaptability of IP, establishes a value transition model from core content to derivative forms, and uses multi-platform communication dynamics analysis to accurately identify the content consumption preferences of different audience groups and guide the targeted placement strategy of derivatives on short videos and social media. This full-link transformation mechanism, which runs through content incubation, derivative development, and commercial operations, not only enhances the commercial monetization capabilities of cultural IP, but also promotes the continuous release of content value in the spatial and temporal dimensions, forming a multi-dimensional and three-dimensional cultural industry ecosystem.

[0041] 5. In the present invention, the AI-driven digital publishing content and online derivative resource performance prediction system, the integrated model explainability module ensures that the algorithm prediction process conforms to human cognitive logic through visual attribution and decision path tracing. The privacy compliance engine uses advanced anonymization processing technology to build a security line in the data collection and use links. The fairness constraint algorithm actively eliminates potential bias and maintains the diversity of content recommendations. This design concept that gives equal importance to technological advancement and ethical considerations not only ensures the scientific nature of business decisions, but also adheres to the social responsibility of cultural communication, setting a reliable technical benchmark for the intelligent transformation of the digital publishing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a system block diagram of an AI-driven digital publishing content and online derivative resource performance prediction system proposed by the present invention. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0044] Reference Figure 1 , an AI-driven performance prediction system for digital publishing content and online derivative resources, including:

[0045] Multimodal data collection layer: Real-time capture of publication content metadata, user reading history, social media UGC, and search engine trends through API interfaces;

[0046] Feature engineering engine, which includes text feature engine, visual feature engine and time series feature engine;

[0047] The prediction model cluster includes a basic prediction module, a generation enhancement module, and a dynamic feedback system. The basic prediction module models cross-modal associations based on the Transformer's multi-head attention mechanism. The generation enhancement module fine-tunes the Stable Diffusion algorithm to generate derivative resource prototypes. The dynamic feedback system receives market data in real time to update model parameters.

[0048] The content understanding and semantic parsing module analyzes the deep semantics and emotional associations of multimodal data to build a structured cognitive framework to support content value assessment;

[0049] Intelligent decision-making and resource scheduling modules enable dynamic resource allocation and copyright protection based on prediction results, optimizing management decisions throughout the entire life cycle of published content.

[0050] The virtual-reality fusion simulation module builds a virtual market environment to simulate content dissemination paths, verify the feasibility of derivative strategies, and predict potential risks;

[0051] The value chain extension module quantitatively evaluates the cross-domain development potential of content, guides the development path of derivatives, and realizes the full-chain transformation of cultural value into commercial value.

[0052] In this embodiment, the text feature engine uses RoBERTa-Large to extract semantic vectors and construct a narrative feature matrix with more than 200 dimensions. The visual feature engine uses the CLIP model to analyze visual elements such as cover design and illustration style. The temporal feature engine uses LSTM to capture the life cycle curve of content popularity.

[0053] In this embodiment, the content understanding and semantic parsing module includes:

[0054] The semantic depth analysis unit uses knowledge graph technology to analyze entity relationships in the text, construct a three-dimensional semantic network of author-subject-reader group, receive text features output by the feature engineering engine, and provide structured semantic data to the prediction model;

[0055] The sentiment tendency discrimination unit detects the sentiment polarity of user comments through a pre-trained language model, establishes a time series of sentiment fluctuations, and interacts with the time series feature engine to jointly construct a content popularity decay model;

[0056] The cross-modal alignment unit calculates the semantic consistency score between the cover visual elements and the text theme, generates a cross-modal alignment matrix, provides image-text matching indicators to the generation enhancement module, and guides the design of derivatives.

[0057] In this embodiment, the intelligent decision-making and resource scheduling module includes:

[0058] The resource priority assessment unit automatically divides resources into S / A / B / C levels based on the content value prediction results, generates resource delivery strategies, receives the output of the prediction model cluster, and pushes resource allocation plans to publishing organizations;

[0059] The dynamic pricing calculation unit combines market demand forecasts and historical price curves to build a simulation pricing model, share user behavior data with the feature engineering engine, and provide decision support for derivatives pricing;

[0060] The copyright intelligent management unit automatically detects content copyright features, generates digital watermark embedding solutions and infringement monitoring rules, and works in conjunction with the multimodal data collection layer to scan online infringing content in real time.

[0061] In this embodiment, the virtual-reality fusion simulation module includes:

[0062] The digital twin construction unit creates a virtual publishing market environment, simulates the content dissemination path under different marketing strategies, receives the initial parameters output by the prediction model, and feeds back the simulation results to optimize the model;

[0063] The risk stress testing unit injects extreme market volatility parameters to assess the content's risk resistance and the effectiveness of emergency plans, forming a closed loop with the dynamic feedback system to improve the model's robustness;

[0064] The virtual user behavior simulation unit generates virtual reader groups with different demographic characteristics, tests content acceptance, and provides test samples for derivative product design to generate enhancement modules.

[0065] In this embodiment, the value chain extension module includes:

[0066] The IP value assessment unit quantitatively analyzes the derivative development potential of content IP, generates a fitness score, and works in conjunction with the generation enhancement module to screen high-value derivative development directions;

[0067] The cross-platform communication analysis unit builds a multi-platform communication dynamics model, predicts the fission index of content in channels such as short videos and social media, and provides channel selection recommendations for the resource scheduling module;

[0068] The life cycle management unit tracks the entire cycle data of content from listing to delisting, automatically generates reprint / reproduction decision recommendations, and pushes content decline warning signals to the dynamic feedback system.

[0069] In this embodiment, the multimodal data acquisition layer is connected to a data governance enhancement layer and a model interpretability module. The data governance enhancement layer includes:

[0070] Heterogeneous data cleaning unit deploys an adaptive data cleaning pipeline, develops an adversarial training denoising model for noisy user review data, establishes a multi-source data confidence assessment system, and dynamically allocates data weights;

[0071] The privacy compliance engine integrates a differential privacy mechanism, performs k-anonymization on user reading trajectories, develops a copyright data desensitization algorithm, and automatically blurs sensitive text fragments;

[0072] In this embodiment, the model interpretability module includes:

[0073] The prediction traceability visualization unit generates a heat attribution map to display the key feature dimensions that affect market predictions, builds a decision tree mapping model, and explains the derivation path of the success probability of derivatives;

[0074] The bias detection and correction unit uses a fairness constraint algorithm to eliminate potential biases such as author gender and subject type, and develops content diversity evaluation indicators to prevent homogeneous recommendations.

[0075] In this embodiment, the data collection and governance layer deploys a distributed crawler cluster connected to the publishing platform API to obtain metadata and user interaction logs for digital content such as e-books and audiobooks in real time. Social media user-generated content (UGC) is captured using a sentiment-enhanced crawler that dynamically filters noisy data. After processing heterogeneous data through an adaptive cleaning pipeline, differential privacy algorithms are used to desensitize user reading trajectories and generate a standardized multimodal dataset. During the feature modeling phase, the text feature engine uses RoBERTa-Large to extract semantic features such as narrative tension and thematic depth. The visual engine uses the CLIP model to encode the style vector of the cover design. The time series engine uses a bidirectional LSTM to capture the nonlinear fluctuations in content popularity. The cross-modal alignment unit calculates the semantic consistency index between image and text and constructs a spatiotemporal feature fusion tensor. The basic prediction module of the prediction and generation system deploys a Transformer architecture and uses a multi-head attention mechanism to establish a dynamic correlation model between text features, visual elements, and user behavior. The generation enhancement module, based on a fine-tuned Stable Diffusion model, inputs a cross-modal alignment matrix to generate derivative visual prototypes. This is then combined with a virtual user behavior simulation unit for A / B testing. The dynamic feedback system updates the prediction model weights based on real-time market data through an online learning mechanism. The intelligent decision-making module of the decision-making and simulation layer uses Monte Carlo simulation to build dynamic pricing strategies, derives copyright protection rules based on knowledge graphs, and automatically generates digital watermark embedding solutions. The virtual-reality fusion simulation module creates a virtual market twin, injects historical communication data to train the communication dynamics model, simulates the content diffusion path under different marketing strategies, and verifies the risk resistance of derivatives through adversarial sample testing. The IP value assessment unit of the value conversion system quantitatively analyzes the cross-domain adaptability of factors such as character settings and worldview architecture, and generates feasibility reports for paths such as film and television adaptation and peripheral development. The lifecycle management module tracks the content decay curve, triggers the reprint decision algorithm to generate a secondary marketing strategy, and pushes infringement feature vectors to the copyright monitoring network to achieve closed-loop management of commercial value and copyright protection.

[0076] The above is a detailed introduction to the AI-driven digital publishing content and online derivative resource performance prediction system provided by the present invention. Specific embodiments are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. An AI-driven digital publishing content and online derivative resource performance prediction system, characterized by: include: Multimodal data collection layer: Real-time capture of publication content metadata, user reading history, social media UGC, and search engine trends through API interfaces; Feature engineering engine, including text feature engine, visual feature engine and time series feature engine; A prediction model cluster, comprising a basic prediction module, a generation enhancement module, and a dynamic feedback system. The basic prediction module models cross-modal associations based on the Transformer's multi-head attention mechanism, while the generation enhancement module fine-tunes the Stable Diffusion algorithm to generate derivative resource prototypes. The dynamic feedback system receives market data in real time to update model parameters. The content understanding and semantic parsing module analyzes the deep semantics and emotional associations of multimodal data to build a structured cognitive framework to support content value assessment; Intelligent decision-making and resource scheduling modules enable dynamic resource allocation and copyright protection based on prediction results, optimizing management decisions throughout the entire life cycle of published content. The virtual-reality fusion simulation module builds a virtual market environment to simulate content dissemination paths, verify the feasibility of derivative strategies, and predict potential risks; The value chain extension module quantitatively evaluates the cross-domain development potential of content, guides the development path of derivatives, and realizes the full-chain transformation of cultural value into commercial value.

2. The AI-driven digital publishing content and online derivative resource performance prediction system according to claim 1, characterized in that: The text feature engine uses RoBERTa-Large to extract semantic vectors and construct a narrative feature matrix with more than 200 dimensions. The visual feature engine uses the CLIP model to analyze visual elements such as cover design and illustration style. The temporal feature engine uses LSTM to capture the life cycle curve of content popularity.

3. The AI-driven digital publishing content and online derivative resource performance prediction system according to claim 1, characterized in that: The content understanding and semantic parsing module includes: The semantic depth analysis unit uses knowledge graph technology to analyze entity relationships in the text, construct a three-dimensional semantic network of author-subject-reader group, receive text features output by the feature engineering engine, and provide structured semantic data to the prediction model; The sentiment tendency discrimination unit detects the sentiment polarity of user comments through a pre-trained language model, establishes a time series of sentiment fluctuations, and interacts with the time series feature engine to jointly construct a content popularity decay model; The cross-modal alignment unit calculates the semantic consistency score between the cover visual elements and the text theme, generates a cross-modal alignment matrix, provides image-text matching indicators to the generation enhancement module, and guides the design of derivatives.

4. The AI-driven digital publishing content and online derivative resource performance prediction system according to claim 1, characterized in that: The intelligent decision-making and resource scheduling module includes: The resource priority assessment unit automatically divides resources into S / A / B / C levels based on the content value prediction results, generates resource delivery strategies, receives the output of the prediction model cluster, and pushes resource allocation plans to publishing organizations; The dynamic pricing calculation unit combines market demand forecasts and historical price curves to build a simulation pricing model, share user behavior data with the feature engineering engine, and provide decision support for derivatives pricing; The copyright intelligent management unit automatically detects content copyright features, generates digital watermark embedding solutions and infringement monitoring rules, and works in conjunction with the multimodal data collection layer to scan online infringing content in real time.

5. The AI-driven digital publishing content and online derivative resource performance prediction system according to claim 1, characterized in that: The virtual-reality fusion simulation module includes: The digital twin construction unit creates a virtual publishing market environment, simulates the content dissemination path under different marketing strategies, receives the initial parameters output by the prediction model, and feeds back the simulation results to optimize the model; The risk stress testing unit injects extreme market volatility parameters to assess the content's risk resistance and the effectiveness of emergency plans, forming a closed loop with the dynamic feedback system to improve the model's robustness; The virtual user behavior simulation unit generates virtual reader groups with different demographic characteristics, tests content acceptance, and provides test samples for derivative product design to generate enhancement modules.

6. The AI-driven digital publishing content and online derivative resource performance prediction system according to claim 1, characterized in that: The value chain extension module includes: The IP value assessment unit quantitatively analyzes the derivative development potential of content IP, generates a fitness score, and works in conjunction with the generation enhancement module to screen high-value derivative development directions; The cross-platform communication analysis unit builds a multi-platform communication dynamics model, predicts the fission index of content on channels such as short videos and social media, and provides channel selection recommendations for the resource scheduling module; The life cycle management unit tracks the entire cycle data of content from listing to delisting, automatically generates reprint / reproduction decision recommendations, and pushes content decline warning signals to the dynamic feedback system.

7. The AI-driven digital publishing content and online derivative resource performance prediction system according to claim 1, characterized in that: The multimodal data acquisition layer is connected to a data governance enhancement layer and a model interpretability module. The data governance enhancement layer includes: Heterogeneous data cleaning unit deploys an adaptive data cleaning pipeline, develops an adversarial training denoising model for noisy user review data, establishes a multi-source data confidence assessment system, and dynamically allocates data weights; The privacy compliance engine integrates a differential privacy mechanism, performs k-anonymization on user reading trajectories, develops a copyright data desensitization algorithm, and automatically blurs sensitive text fragments.

8. The AI-driven digital publishing content and online derivative resource performance prediction system according to claim 1, characterized in that: The model interpretability module includes: The prediction traceability visualization unit generates a heat attribution map to display the key feature dimensions that affect market predictions, builds a decision tree mapping model, and explains the derivation path of the success probability of derivatives; The bias detection and correction unit uses a fairness constraint algorithm to eliminate potential biases such as author gender and subject type, and develops content diversity evaluation indicators to prevent homogeneous recommendations.

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