Method and system for analyzing and evaluating publishing content based on natural language processing

Abstract

This invention relates to the field of natural language processing (NLP) technology, and more particularly to a method and system for analyzing and evaluating the quality of published content based on NLP. The method involves acquiring the text of the published content to be analyzed, dividing it into semantic units, and extracting knowledge elements and their relationships; constructing a multi-dimensional knowledge density representation model, and generating a density representation result of knowledge carrying efficiency by coupling the independence measure and the tightness measure of the relationships between knowledge elements within semantic units; performing density distribution analysis based on the density representation result, identifying abnormal patterns and establishing a density optimization decision mechanism; generating a content adjustment scheme that maintains the integrity of the knowledge system based on the density gradient change trend and the transmission path of the relationships between knowledge elements; ensuring the traceability of the knowledge link through continuity verification of the transmission path; executing the adjustment scheme and obtaining feedback data; and iteratively optimizing the calculation rules for the independence and tightness measures.

Description

Technical Field

[0001] This invention relates to the field of natural language processing technology, and in particular to a method and system for analyzing and evaluating the quality of published content based on natural language processing. Background Technology

[0002] In the field of content analysis and quality assessment, current practices primarily rely on manual editing and text analysis methods based on statistical rules. Manual editing typically involves professional editors reviewing the text word by word, relying on experience to judge the logical coherence, accuracy of knowledge, and clarity of expression, and then providing suggestions for revision. Methods based on statistical rules utilize shallow features such as word frequency, sentence length, and readability formulas to quantitatively score the text; for example, they assess content quality by calculating lexical richness or sentence complexity. These methods are widely used in traditional publishing processes to ensure the basic standardization of publications.

[0003] However, existing practices have significant drawbacks. Manual review relies heavily on editors' subjective experience, and different editors may have significantly different evaluation criteria for the same content, leading to a lack of consistency and repeatability in quality assessment results. Furthermore, manual methods are inefficient and struggle to handle the batch processing demands of large-scale publications. While statistical rule-based methods can achieve a degree of automation, their analytical dimensions are limited, focusing only on surface-level textual features and failing to delve into the knowledge structure and relationships within semantic units. For example, these methods struggle to distinguish the degree of independence of knowledge elements or quantify the tightness of relationships between them, thus failing to accurately assess the knowledge-carrying efficiency of the content. When content exhibits uneven knowledge density distribution or broken key knowledge links, statistical rules often fail to effectively identify these deeper issues, leading to optimization schemes that compromise the integrity of the knowledge system or render the adjusted content lacking traceability. Summary of the Invention

[0004] This invention provides a method and system for analyzing and evaluating the quality of published content based on natural language processing, which can solve the problems in the prior art.

[0005] A first aspect of this invention provides a method for analyzing and assessing the quality of published content based on natural language processing, comprising: Obtain the published content text to be analyzed, divide the published content text into semantic units, and extract the knowledge elements and their relationships in each semantic unit; A multi-dimensional knowledge density representation model is constructed. The multi-dimensional knowledge density representation model performs coupled calculations by measuring the independence of knowledge elements within a semantic unit and the tightness of the relationship between knowledge elements, thereby generating a density representation result that reflects the knowledge carrying efficiency. Based on the density characterization results, density distribution analysis is performed on the published content text to identify abnormal patterns in density distribution, establish a density optimization decision mechanism, generate a content adjustment scheme under the constraint of maintaining the integrity of the knowledge system based on the changing trend of density gradient in the abnormal patterns and the transmission path of the relationship between knowledge elements, and ensure the traceability of the adjusted knowledge link through the continuity verification of the transmission path. The content adjustment scheme is executed and feedback data on the adjustment effect is obtained. The feedback data is then used to iteratively optimize the independence measurement calculation rules and the tightness measurement calculation rules in the multi-dimensional knowledge density representation model.

[0006] A multi-dimensional knowledge density representation model is constructed. This model couples the independence measure of knowledge elements within semantic units with the tightness measure of the relationship between knowledge elements to generate a density representation result that reflects the knowledge carrying efficiency, including: For each semantic unit, the independence measure of each knowledge element within it is calculated. The independence measure quantifies the comprehensibility of the knowledge element in the absence of context by assessing the degree of dependence of the knowledge element on the external context of the semantic unit. The tightness measure of the relationship between knowledge elements within the semantic unit is calculated. The tightness measure quantifies the coupling strength between knowledge elements by evaluating the necessity of logical deduction between knowledge elements and the directness of the deduction path. The independence measure is used as the basic contribution weight of knowledge elements, and the tightness measure is used as the synergistic gain coefficient between knowledge elements. Through the combination operation of the basic contribution weight and the synergistic gain coefficient, a density value representing the effective knowledge carrying capacity within a unit text length is generated. For all semantic units in the published content text, the density value of each semantic unit is calculated, and the density value is associated and mapped with the position information and hierarchical information of the semantic unit to form a density representation result that includes spatial distribution features and hierarchical distribution features.

[0007] The density value representing the effective knowledge carrying capacity per unit text length is generated by combining the basic contribution weight and the collaborative gain coefficient, including: For each knowledge element within a semantic unit, extract the basic contribution weight of that knowledge element and the collaborative gain coefficient between that knowledge element and its associated knowledge elements. A weight gain coupling calculation expression is established. Based on the weight gain coupling calculation expression, the basic contribution weight is used as the initial contribution component, and the product of the collaborative gain coefficient and the basic contribution weight of the associated knowledge element is used as the collaborative contribution component. The comprehensive contribution value of the knowledge element is obtained by weighted summation of the initial contribution component and the collaborative contribution component. The total knowledge carrying capacity of the semantic unit is obtained by summing the comprehensive contribution values ​​of all knowledge elements within the semantic unit. The total knowledge carrying capacity is adjusted by applying a marginal decrease correction. By constructing a decay function that reflects the impact of knowledge element density on carrying efficiency, and based on the distribution characteristics of the number of knowledge elements in the semantic unit and the cooperative gain coefficient, the total knowledge carrying capacity is nonlinearly adjusted to generate the corrected total knowledge carrying capacity. Divide the total amount of knowledge carried by the modified semantic unit by the text length to obtain the density value.

[0008] Based on the density characterization results, density distribution analysis is performed on the published content text to identify abnormal patterns in the density distribution and establish a density optimization decision-making mechanism, including: Based on the density values ​​of each semantic unit in the density characterization results, a density distribution map reflecting the spatial and hierarchical dimensions of the published content is constructed. Morphological features are extracted from the density distribution map, and abnormal patterns in the density distribution are identified by detecting local extrema and gradient abrupt change points in the density values. By tracing the knowledge element relationships of semantic units within the region corresponding to the abnormal pattern, analyzing the breakage and clustering characteristics of the knowledge element relationships, determining the knowledge structural root cause of the abnormal pattern, and establishing a density optimization decision mechanism.

[0009] Based on the transmission path of the density gradient change trend and the relationship between knowledge elements in the aforementioned abnormal patterns, a content adjustment scheme is generated under the constraint of maintaining the integrity of the knowledge system, including: For the aforementioned abnormal pattern, the density gradient change trend at the boundary of the abnormal region is extracted, and based on the density gradient change trend, the target semantic unit that needs to be density adjusted and its adjustment target direction are determined. Tracing the relationship transmission path of knowledge elements within the target semantic unit, and extracting necessary transmission nodes in the logical deduction chain by identifying the logical deduction chain between the starting knowledge element and the ending knowledge element in the relationship transmission path; Establish knowledge system integrity constraint rules, and set the association relationship between the necessary transmission node and its adjacent knowledge elements as an adjustment prohibited area according to the knowledge system integrity constraint rules; Based on the prohibited adjustment regions, and under the constraints of the knowledge system integrity rules, a content adjustment scheme is generated for the target semantic unit.

[0010] Establish knowledge system integrity constraint rules, and set the association relationship between the necessary transmission node and its adjacent knowledge elements as an adjustment prohibited area according to the knowledge system integrity constraint rules, including: For the necessary transmission node, identify the predecessor and successor knowledge elements directly associated with the necessary transmission node, and construct a local association network centered on the necessary transmission node; Deductive dependency analysis is performed on the relationships in the local association network. By evaluating the degree of damage to the logical integrity of the transmission path after deleting or modifying a certain relationship, the relationships that support the continuity of the transmission path are identified. Based on the aforementioned relationships, knowledge system integrity constraint rules are established, and based on these rules, the knowledge element pairs involved in the relationships and their logical dependencies are set as prohibited adjustment areas.

[0011] A second aspect of this invention provides a publishing content analysis and quality assessment system based on natural language processing, comprising: The knowledge extraction unit is used to acquire the published content text to be analyzed, divide the published content text into semantic units, and extract the knowledge elements and their relationships in each semantic unit. A density representation unit is used to construct a multi-dimensional knowledge density representation model. The multi-dimensional knowledge density representation model performs coupled calculations by measuring the independence of knowledge elements within a semantic unit and the tightness of the relationship between knowledge elements, generating a density representation result that reflects the knowledge carrying efficiency. The optimization decision unit is used to perform density distribution analysis on the published content text based on the density characterization results, identify abnormal patterns in the density distribution, establish a density optimization decision mechanism, generate a content adjustment scheme under the constraint of maintaining the integrity of the knowledge system based on the change trend of the density gradient in the abnormal pattern and the transmission path of the relationship between the knowledge elements, and ensure the traceability of the adjusted knowledge link through the continuity verification of the transmission path. The iterative adjustment unit is used to execute the content adjustment scheme and obtain feedback data on the adjustment effect, and to use the feedback data to iteratively optimize the independence measurement calculation rules and the tightness measurement calculation rules in the multi-dimensional knowledge density representation model.

[0012] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0014] This method can accurately quantify the knowledge carrying efficiency of published content. By coupling the measurement of the independence of knowledge elements within semantic units with the measurement of the tightness of their relationships, it generates objective density representation results, avoiding the subjective bias of traditional manual evaluation. Density distribution analysis can quickly locate areas of knowledge redundancy or deficiency, identify abnormal patterns, provide data-driven evidence for content optimization, and improve the scientific rigor and consistency of the evaluation.

[0015] Based on the analysis of density gradient change trends and knowledge element transmission paths, targeted adjustment plans can be generated while maintaining the integrity of the knowledge system. Verification of the continuity of transmission paths ensures the traceability of the adjusted knowledge chain, avoids knowledge gaps or logical jumps, guarantees the rigor of the content structure and the smoothness of knowledge transmission, and significantly reduces the risk of subsequent modifications.

[0016] The feedback-driven iterative optimization mechanism endows the model with self-learning capabilities. The calculation rules for independence and tightness measures are continuously updated based on actual adjustment effects, making the density representation results more consistent with the characteristic distribution of published content in different fields. This dynamic optimization characteristic enables the method to have cross-genre and cross-disciplinary generalization adaptability, and long-term use can continuously improve the evaluation accuracy and efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the method for analyzing and assessing the quality of published content based on natural language processing, as described in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0020] Figure 1 This is a flowchart illustrating the method for analyzing and assessing the quality of published content based on natural language processing, as described in this embodiment of the invention. Figure 1 As shown, the methods for publishing content analysis and quality assessment based on natural language processing include: Obtain the published content text to be analyzed, divide the published content text into semantic units, and extract the knowledge elements and their relationships in each semantic unit; A multi-dimensional knowledge density representation model is constructed. The multi-dimensional knowledge density representation model performs coupled calculations by measuring the independence of knowledge elements within a semantic unit and the tightness of the relationship between knowledge elements, thereby generating a density representation result that reflects the knowledge carrying efficiency. Based on the density characterization results, density distribution analysis is performed on the published content text to identify abnormal patterns in density distribution, establish a density optimization decision mechanism, generate a content adjustment scheme under the constraint of maintaining the integrity of the knowledge system based on the changing trend of density gradient in the abnormal patterns and the transmission path of the relationship between knowledge elements, and ensure the traceability of the adjusted knowledge link through the continuity verification of the transmission path. The content adjustment scheme is executed and feedback data on the adjustment effect is obtained. The feedback data is then used to iteratively optimize the independence measurement calculation rules and the tightness measurement calculation rules in the multi-dimensional knowledge density representation model.

[0021] In one optional implementation, a multi-dimensional knowledge density representation model is constructed. This model couples the independence measure of knowledge elements within a semantic unit with the tightness measure of the relationship between knowledge elements to generate a density representation result reflecting knowledge carrying efficiency, including: For each semantic unit, the independence measure of each knowledge element within it is calculated. The independence measure quantifies the comprehensibility of the knowledge element in the absence of context by assessing the degree of dependence of the knowledge element on the external context of the semantic unit. The tightness measure of the relationship between knowledge elements within the semantic unit is calculated. The tightness measure quantifies the coupling strength between knowledge elements by evaluating the necessity of logical deduction between knowledge elements and the directness of the deduction path. The independence measure is used as the basic contribution weight of knowledge elements, and the tightness measure is used as the synergistic gain coefficient between knowledge elements. Through the combination operation of the basic contribution weight and the synergistic gain coefficient, a density value representing the effective knowledge carrying capacity within a unit text length is generated. For all semantic units in the published content text, the density value of each semantic unit is calculated, and the density value is associated and mapped with the position information and hierarchical information of the semantic unit to form a density representation result that includes spatial distribution features and hierarchical distribution features.

[0022] After acquiring and completing the semantic unit segmentation and knowledge element extraction, a multi-dimensional representation of knowledge density is performed for each semantic unit. For a single semantic unit, the independence characteristics of each knowledge element contained therein are first analyzed. The independence measure of a knowledge element reflects its self-explanatory ability after being removed from the current semantic environment. The specific evaluation process needs to examine the degree of dependence of the knowledge element on the external context. For a knowledge element, the number of pre-concepts required for its complete expression is counted, and it is analyzed whether these pre-concepts are defined within the current semantic unit. If the expression of the knowledge element depends on a large number of undefined external terms or background knowledge, its independence measure is low. By constructing a semantic dependency graph of the knowledge element, the in-degree to out-degree ratio of the element node is calculated. The in-degree represents the number of times it is referenced by other elements, and the out-degree represents the number of times it references other elements. When the out-degree is much greater than the in-degree, it indicates that the element is heavily dependent on external information. At the same time, the frequency of use of pronouns and ellipsis structures in the expression of knowledge elements is analyzed. The high frequency of these linguistic phenomena means that content understanding must rely on context reconstruction, which reduces independence. The evaluation results from the above multiple dimensions are weighted and combined to obtain an independence measure value ranging from 0 to 1. The closer the value is to 1, the stronger the self-explanatory ability of the knowledge element.

[0023] After calculating the independence measure, the tightness measure is calculated for the relationships between knowledge elements within the same semantic unit. Relationships between knowledge elements include various types such as causal, progressive, parallel, and contrastive relationships. The tightness measure focuses on evaluating the necessity and directness of these relationships. For two related knowledge elements, the number of intermediate steps required to deduce from one element to the other is analyzed. If the deduction path contains few intermediate nodes and each step has a clear logical basis, the relationship is considered to be highly tight. Semantic role labeling technology is used to identify the functional roles of knowledge elements in syntactic structures. When two knowledge elements respectively play the roles of agent and patient in the same propositional structure, their relationship is considered highly direct. Dependency parsing is used to extract the syntactic dependency path length between knowledge elements. The shorter the path length, the stronger the coupling between the two elements at the linguistic expression level. Simultaneously, the co-occurrence frequency between knowledge elements is examined. The probability of two elements co-occurring in similar contexts is statistically analyzed in a large-scale corpus. A high co-occurrence frequency reflects a stable relationship pattern between the two elements within the knowledge system. By integrating indicators such as the necessity of logical deduction, syntactic dependency distance, and semantic co-occurrence strength from multiple dimensions, a tightness measure value representing the coupling strength between knowledge elements is calculated, and this value is also normalized to the range of 0 to 1.

[0024] After obtaining the independence measure of each knowledge element and the tightness measure of the relationships between elements, a coupled calculation is performed to generate a comprehensive density representation result. The independence measure of a single knowledge element is considered as the basic contribution weight of that element to the knowledge carrying capacity of the semantic unit. Knowledge elements with high independence can convey information without relying on additional interpretation, therefore their information density contribution per unit of text is greater. For semantic units... The knowledge element, denoted as the first... The independence measure of each knowledge element is The text length of this element is Then the fundamental density contribution of this element can be expressed as Building upon this, the synergistic gain effect of relationships between knowledge elements is introduced. Two closely related knowledge elements can form a mutually supportive knowledge structure, improving overall information carrying efficiency. For knowledge elements... and The relationship between them is denoted by the measure of their closeness. The gain contribution of this association to the density is related to the individual basic contributions of the two elements and the degree of their association. The overall density value of the semantic unit is calculated by weighted summation, taking into account both the independent contributions of each knowledge element and the gain term generated by the synergistic effect between elements, ultimately yielding a normalized density value. This value reflects the amount of effective knowledge carried within a unit text length.

[0025] For all semantic units in the published text, their density values ​​are calculated individually, and this density information is correlated with the semantic unit's position within the text. The start and end positions of each semantic unit are recorded, and its relative positional proportion to the entire text is determined, thus constructing a distribution curve of density values ​​as a function of text position. Simultaneously, hierarchical information of the semantic units is extracted. In hierarchical published content, semantic units belong to different chapter levels, such as content under first-level headings, content under second-level headings, etc. A mapping relationship is established between the density values ​​of each semantic unit and its corresponding level. The spatial distribution characteristics of density are presented using a two-dimensional visualization method, with the horizontal axis representing text position and the vertical axis representing density values. Each semantic unit corresponds to a data point in the graph, and the color or marker of the data point further distinguishes semantic units at different levels. For texts with a tree-like hierarchical structure, hierarchical distribution statistics of density are constructed, and statistical measures such as the average density, density variance, and density extreme values ​​of semantic units at each level are calculated to analyze the differences in knowledge density between different levels of content. By integrating density information from both location and hierarchy dimensions, a complete density representation is formed that includes spatial and hierarchical distribution features. This representation not only provides a global density overview but also supports local density analysis for specific location intervals or hierarchical levels, providing a detailed data foundation for subsequent density distribution anomaly pattern recognition and content optimization decisions.

[0026] In constructing the density representation results, differentiated parameter configuration strategies are adopted for different types of published content texts. For academic monographs, the weight of the completeness of professional terminology definitions is increased in the independence measure calculation of knowledge elements, requiring key concepts to be given standardized definitions upon their first appearance, thereby enhancing the self-explanatory ability of terminology. For popular science books, the assessment of the logical deductive continuity between knowledge elements is strengthened in the density measure calculation, ensuring a clear and smooth progression from superficial concepts to deep principles. For reference books, due to their itemized structure, the independence requirements for each semantic unit are higher, and the weight of the independence measure in the density numerical calculation is correspondingly increased. By establishing a mapping rule base between content types and model parameters, the density representation model can be adaptively adjusted to different publishing scenarios, ensuring that the generated density representation results can accurately reflect the knowledge carrying efficiency characteristics of various types of content, providing a reliable basis for subsequent quality assessment and optimization.

[0027] In one optional implementation, generating a density value representing the effective knowledge carrying capacity per unit text length through a combination operation of the basic contribution weight and the collaborative gain coefficient includes: For each knowledge element within a semantic unit, extract the basic contribution weight of that knowledge element and the collaborative gain coefficient between that knowledge element and its associated knowledge elements. A weight gain coupling calculation expression is established. Based on the weight gain coupling calculation expression, the basic contribution weight is used as the initial contribution component, and the product of the collaborative gain coefficient and the basic contribution weight of the associated knowledge element is used as the collaborative contribution component. The comprehensive contribution value of the knowledge element is obtained by weighted summation of the initial contribution component and the collaborative contribution component. The total knowledge carrying capacity of the semantic unit is obtained by summing the comprehensive contribution values ​​of all knowledge elements within the semantic unit. The total knowledge carrying capacity is adjusted by applying a marginal decrease correction. By constructing a decay function that reflects the impact of knowledge element density on carrying efficiency, and based on the distribution characteristics of the number of knowledge elements in the semantic unit and the cooperative gain coefficient, the total knowledge carrying capacity is nonlinearly adjusted to generate the corrected total knowledge carrying capacity. Divide the total amount of knowledge carried by the modified semantic unit by the text length to obtain the density value.

[0028] After obtaining the semantic units and their contained knowledge elements, the density value is calculated for each semantic unit. Taking a semantic unit containing the topic of "machine learning algorithm optimization" as an example, this semantic unit contains 5 knowledge elements, namely gradient descent algorithm, learning rate adjustment strategy, loss function design, regularization method, and hyperparameter optimization technique.

[0029] For the knowledge element of gradient descent algorithm, its basic contribution weight is first extracted. The basic contribution weight is determined based on the importance of the knowledge element's position in the current semantic unit, the strength of its terminology, and the degree of conceptual completeness. As a core algorithmic concept, gradient descent algorithm's basic contribution weight is set to 0.85. Simultaneously, the synergistic gain coefficients between this knowledge element and other knowledge elements are extracted. There is a direct methodological link between gradient descent algorithm and learning rate adjustment strategies, with a synergistic gain coefficient of 0.72; a goal-oriented link with loss function design, with a synergistic gain coefficient of 0.68; an optimization-aiding link with regularization methods, with a synergistic gain coefficient of 0.54; and an indirect supporting link with hyperparameter optimization techniques, with a synergistic gain coefficient of 0.43.

[0030] The basic contribution weight of the learning rate adjustment strategy is 0.78, reflecting its importance as a key means of algorithm tuning. The synergistic gain coefficient between the learning rate adjustment strategy and loss function design is 0.65, with regularization methods it is 0.51, and with hyperparameter optimization techniques it is 0.59.

[0031] Establish the weight gain coupling calculation expression. For the knowledge element of gradient descent algorithm, its initial contribution component is directly equal to its basic contribution weight of 0.85. The calculation of the co-contribution component requires comprehensive consideration of the interaction effects between this knowledge element and all related knowledge elements. The co-contribution component between gradient descent algorithm and learning rate adjustment strategy is calculated as the product of co-contribution gain coefficient of 0.72 and basic contribution weight of learning rate adjustment strategy of 0.78, resulting in 0.5616. Similarly, the co-contribution component between gradient descent algorithm and loss function design is 0.68 multiplied by basic contribution weight of loss function design of 0.82, resulting in 0.5576. The co-contribution component between gradient descent algorithm and regularization method is 0.54 multiplied by basic contribution weight of regularization method of 0.74, resulting in 0.3996. The co-contribution component between gradient descent algorithm and hyperparameter optimization technique is 0.43 multiplied by basic contribution weight of hyperparameter optimization technique of 0.69, resulting in 0.2967.

[0032] After obtaining the initial contribution components and each collaborative contribution component, a weighted sum is performed. The weighting strategy used here is to assign a weight coefficient of 1.0 to the initial contribution component, and to differentiate the weights of the collaborative contribution components based on their correlation strength. The weight coefficient for the collaborative contribution component related to direct methodology is 0.9, the weight coefficient for the collaborative contribution component related to goal-oriented correlation is 0.8, the weight coefficient for the collaborative contribution component related to optimization-assisted correlation is 0.7, and the weight coefficient for the collaborative contribution component related to indirect support correlation is 0.6. The comprehensive contribution value of the gradient descent algorithm is calculated as: 0.85 multiplied by 1.0 plus 0.5616 multiplied by 0.9 plus 0.5576 multiplied by 0.8 plus 0.3996 multiplied by 0.7 plus 0.2967 multiplied by 0.6, resulting in 2.3418.

[0033] Following the same calculation logic, the comprehensive contribution value of the learning rate adjustment strategy is calculated to be 2.1567, the comprehensive contribution value of the loss function design is 2.2834, the comprehensive contribution value of the regularization method is 1.9823, and the comprehensive contribution value of the hyperparameter optimization technique is 1.8645. The comprehensive contribution values ​​of all five knowledge elements within this semantic unit are summed to obtain a total knowledge carrying capacity of 10.6287.

[0034] A marginal diminishing correction is applied to the total knowledge carrying capacity, constructing a decay function that reflects the impact of knowledge element density on carrying efficiency. As the number of knowledge elements within a semantic unit increases, the actual contribution efficiency per unit of knowledge element decreases due to the finite nature of text space and the constraints of reader cognitive load. The decay function design comprehensively considers the distribution characteristics of the number of knowledge elements and the synergistic gain coefficient. This semantic unit contains 5 knowledge elements, with an average synergistic gain coefficient of 0.59 and a standard deviation of 0.11. The decay function adopts a logarithmic decay model, with its expression based on the natural logarithm base. The decay strength parameter is determined based on the dispersion of the synergistic gain coefficient; the more dispersed the distribution of the synergistic gain coefficient, the more diverse the association patterns between knowledge elements, and the weaker the decay strength.

[0035] The specific attenuation calculation process is as follows: Substitute the number of knowledge elements (5) into the logarithmic function to obtain the basic attenuation factor. Simultaneously, calculate the dispersion adjustment coefficient based on the standard deviation of the synergistic gain coefficient (0.11). The larger the standard deviation, the smaller the adjustment coefficient, thus reducing the attenuation level. Multiply the basic attenuation factor by the dispersion adjustment coefficient to obtain the comprehensive attenuation coefficient (0.87). Multiply the total knowledge carrying capacity (10.6287) by the comprehensive attenuation coefficient (0.87) to obtain the corrected total knowledge carrying capacity of 9.2470.

[0036] The text length of this semantic unit is determined by character counting, with a total of 1580 characters including punctuation. Dividing the corrected total knowledge carrying capacity of 9.2470 by the text length of 1580 yields a density value of 0.005853, indicating that each character in this semantic unit carries 0.005853 units of effective knowledge. The dimension of this density value reflects the efficiency level of knowledge carrying; a higher value indicates richer effective knowledge carried per unit text length.

[0037] In practical applications, the criteria for assigning basic contribution weights differ depending on the type of published content. In academic monographs, the rigor of concept definitions and the completeness of theoretical derivations significantly impact the basic contribution weight; in popular science books, the accessibility of knowledge presentation and the fluency of logical connections become important factors in determining the weight; and in technical manuals, the feasibility of operational steps and the accuracy of parameter descriptions are the core criteria for weight evaluation. The calculation of the synergistic gain coefficient also needs to consider the characteristics of the content type. In academic content, citation relationships and reasoning chains contribute significantly to the synergistic gain coefficient, while in practical content, the relevance of application scenarios and problem-solving paths have a greater impact.

[0038] The parameters of the decay function for marginal diminishing returns correction need to be adjusted according to the intended audience of the published content. For content aimed at professional researchers, whose cognitive load tolerance is relatively high, the decay intensity can be appropriately reduced; for content aimed at general readers, to avoid information overload leading to comprehension difficulties, the decay intensity should be increased accordingly. Through this differentiated correction mechanism, it is ensured that the density value can accurately reflect the knowledge carrying efficiency under different application scenarios, providing a reliable quantitative basis for subsequent density distribution analysis and optimization decisions.

[0039] In one optional implementation, density distribution analysis is performed on the published content text based on the density characterization results to identify abnormal patterns in the density distribution and establish a density optimization decision mechanism, including: Based on the density values ​​of each semantic unit in the density characterization results, a density distribution map reflecting the spatial and hierarchical dimensions of the published content is constructed. Morphological features are extracted from the density distribution map, and abnormal patterns in the density distribution are identified by detecting local extrema and gradient abrupt change points in the density values. By tracing the knowledge element relationships of semantic units within the region corresponding to the abnormal pattern, analyzing the breakage and clustering characteristics of the knowledge element relationships, determining the knowledge structural root cause of the abnormal pattern, and establishing a density optimization decision mechanism.

[0040] The density representation results are obtained after the published content text to be analyzed is calculated by a multi-dimensional knowledge density representation model. These results include the density value corresponding to each semantic unit, a list of knowledge elements, and the strength values ​​of their interrelationships. Taking a science and technology book as an example, this book contains 12 chapters, which are divided into 256 semantic units. The density value of each semantic unit ranges from 0.12 to 0.87. Specifically, the semantic unit density value for section 2 of chapter 3 is 0.82, and the semantic unit density value for section 5 of chapter 7 is 0.15.

[0041] Spatial location and hierarchical information of each semantic unit in the density representation results were extracted. Spatial location information was determined by the chapter number, paragraph number, and character start position of the semantic unit in the published text. Hierarchical information was divided into three levels according to the logical structure of the content: thematic level, argumentative level, and detail level. The 256 semantic units were unfolded on the horizontal axis according to chapter order, and the density values ​​were represented on the vertical axis using logarithmic coordinates, forming a one-dimensional density distribution curve. Based on this, a hierarchical dimension was introduced, with thematic level semantic units marked in red, argumentative level semantic units marked in blue, and detail level semantic units marked in green, constructing a two-dimensional density distribution map of the published text's spatial and hierarchical dimensions. This map clearly shows that the overall density value of Chapter 3 remains in the range of 0.65 to 0.82, while the overall density value of Chapter 7 is distributed in the range of 0.15 to 0.38, showing a significant difference between the two.

[0042] Morphological feature extraction was performed on the density distribution map using a sliding window mechanism that traversed all semantic units along the spatial dimension. The sliding window length was set to 7 semantic units, with a step size of 1 semantic unit. The mean and variance of the density values ​​within each window position were calculated. When the window moved to the 78th semantic unit position, the density values ​​of the 7 semantic units within the window were 0.71, 0.74, 0.69, 0.73, 0.28, 0.31, and 0.29, respectively, with a calculated mean of 0.54 and a variance of 0.045. Moving the window further to the 79th semantic unit position, the density values ​​within the window became 0.74, 0.69, 0.73, 0.28, 0.31, 0.29, and 0.27, with the mean decreasing to 0.47 and the variance remaining at 0.043. Local extrema are detected by comparing the means of consecutive windows. When the mean of a given window is the maximum or minimum relative to the means of the three windows before and after it, the center of that window is marked as a local extrema. In the example above, the window mean at the 81st semantic unit is 0.33, and the mean values ​​of the three windows before and after it are 0.47, 0.42, 0.38 and 0.35, 0.37, 0.41, respectively. This position is marked as a local minimum.

[0043] The difference in density values ​​between adjacent semantic units is used to construct a gradient sequence. The gradient sequence is then... element Indicates the first The semantic unit and the first The density difference of each semantic unit. Performing a second-order difference operation on the gradient sequence yields the gradient rate of change sequence. When the absolute value of the gradient rate of change exceeds a set threshold of 0.15, a gradient abrupt change is determined to exist at that position. In the semantic unit interval from 78 to 84, the gradient sequence is 0.03, -0.05, 0.04, -0.45, 0.03, -0.02, -0.02, and the gradient rate of change sequence is -0.08, 0.09, -0.49, 0.48, -0.05, 0. Among them, the gradient rate of change at positions 80 to 81 is -0.49, and the gradient rate of change at positions 81 to 82 is 0.48. Both absolute values ​​exceed the threshold, thus marking a gradient abrupt change point in the semantic unit interval from 80 to 82.

[0044] By combining the distribution locations of local extrema and gradient mutation points, abnormal patterns in density distribution are identified. An area is judged as an abnormal pattern when it meets the following conditions simultaneously: the density values ​​of more than 5 consecutive semantic units are lower than 60% of the global density mean, or more than 3 consecutive semantic units have gradient mutation points, or there is a local extrema and the deviation of the density value of the extrema from the density mean of the adjacent area exceeds 40%. In the aforementioned science and technology book example, the density values ​​of semantic units 78 to 88 are 0.71, 0.74, 0.69, 0.73, 0.28, 0.31, 0.29, 0.27, 0.26, 0.32, and 0.34, respectively, with a global density mean of 0.52. The density values ​​of semantic units 82 to 86 in this interval are all below 0.31, which is below the 60% threshold of 0.312 of the global mean, for a total of 5 semantic units. At the same time, semantic unit 81 is marked as a local extreme point, and its density value of 0.28 deviates from the mean of the preceding region of 0.72 by 61%, which meets the abnormal pattern recognition conditions. Therefore, this interval is marked as abnormal pattern region A1.

[0045] Tracing the knowledge elements and their relationships within the semantic units corresponding to the anomalous pattern region A1, semantic units 78 to 81 contain knowledge elements E1 to E23, with 37 relationships between them, and the relationship strengths ranging from 0.45 to 0.82. Semantic units 82 to 88 contain knowledge elements E24 to E41, with only 12 relationships between them, and the relationship strengths ranging from 0.21 to 0.38. Examining the relationships between knowledge elements across regions, it was found that there is no direct relationship between knowledge elements E23 and E24. E23's associated objects are E19, E21, and E22, while E24's associated objects are E26 and E28, and their associated object sets have no overlap. Further tracing back, E19's associated objects include E12 and E15, and E26's associated objects include E29 and E31, but no connection path was found. This phenomenon represents a break in the relationships between knowledge elements, meaning that the knowledge elements before and after the anomalous pattern region lack a transmission path, resulting in an interruption of the knowledge link.

[0046] Analyzing the distribution of knowledge elements within the anomalous pattern region A1, the 18 knowledge elements in semantic units 82 to 88 exhibit a clear clustering phenomenon in the semantic space. Semantic vector representations of knowledge elements are used, and a similarity matrix is ​​constructed by calculating the cosine similarity between knowledge elements. When the similarity exceeds 0.75, two knowledge elements are considered to have highly overlapping semantics. In E24 to E41, the similarities between E24, E26, and E28 are 0.81, 0.79, and 0.83, respectively, while the similarities between E31, E33, E35, and E37 are all greater than 0.76, forming two semantic clusters. This clustering characteristic indicates insufficient independence of knowledge elements within this region, with repetitive descriptions occupying too much semantic unit space, leading to a decrease in knowledge density.

[0047] The structural knowledge roots of the abnormal patterns were identified. Analysis of region A1 revealed that the breakage characteristic stemmed from the lack of transitional knowledge elements connecting preceding and following content, resulting in excessive logical jumps between topics. The clustering characteristic arose from over-elaboration of a particular knowledge point, introducing numerous knowledge elements with similar semantics and diluting the effective information capacity per unit space. Based on this root cause analysis, a density optimization decision mechanism was established, comprising a breakage repair strategy and a clustering simplification strategy.

[0048] The break repair strategy reconstructs the knowledge transfer path by inserting bridging knowledge elements at the break points. For the break between E23 and E24, knowledge elements with associations to both E23 and E24 are retrieved from the candidate knowledge element library. The candidate element E_bridge has an association strength of 0.52 with E23 and 0.48 with E24. E_bridge is inserted as a bridging element between semantic units 81 and 82, forming the transfer path E23-E_bridge-E24. Verifying the continuity of the inserted path, E23 can be traced back to E24 via E_bridge, and E24 can be traced back to E23 via E_bridge, satisfying the bidirectional traceability requirement.

[0049] The clustering and simplification strategy merges or deletes semantically overlapping knowledge elements. For the clusters E24, E26, and E28, E24, which has the most information, is retained as the representative element, and the associations between E26 and E28 are transferred to E24. Specifically, the original associations of E26, including E26-E29 and E26-E30, are transferred to E24-E29 and E24-E30, with the association strength calculated by multiplying the original association strength by a similarity conversion factor of 0.81. The same transfer operation is performed on the associations of E28. For the clusters E31, E33, E35, and E37, common features of the four elements are extracted to construct a fused knowledge element E_fusion. E_fusion inherits all associations of the four elements, and the association strength is the average of the original association strengths. Through streamlined operations, the number of knowledge elements in semantic units 82 to 88 was reduced from 18 to 11, while the number of related relationships increased from 12 to 23. The density value of this region is expected to increase to the range of 0.48 to 0.55.

[0050] The established density optimization decision-making mechanism integrates fracture repair strategies and clustering simplification strategies, forming a rule base and execution process. The rule base defines the strategy selection logic corresponding to different anomaly pattern types. When a fracture feature is detected, a bridging element retrieval and insertion process is triggered; when a clustering feature is detected, a similarity calculation and element merging process is triggered. The execution process includes five stages: anomaly detection, root cause localization, strategy matching, scheme generation, and effect prediction. Each stage outputs intermediate results for subsequent stages to use. This decision-making mechanism supports batch processing of multiple anomaly pattern regions. When processing all anomaly regions in Chapter 7, it identified 4 fracture feature locations and 6 clustering feature regions, generating 12 corresponding content adjustment schemes. The overall density distribution variance is expected to decrease from 0.052 to 0.031, significantly improving the uniformity of the density distribution.

[0051] In one optional implementation, a knowledge system integrity constraint rule is established, and the association relationship between the necessary transmission node and its adjacent knowledge elements is set as an adjustment prohibited area according to the knowledge system integrity constraint rule, including: For the necessary transmission node, identify the predecessor and successor knowledge elements directly associated with the necessary transmission node, and construct a local association network centered on the necessary transmission node; Deductive dependency analysis is performed on the relationships in the local association network. By evaluating the degree of damage to the logical integrity of the transmission path after deleting or modifying a certain relationship, the relationships that support the continuity of the transmission path are identified. Based on the aforementioned relationships, knowledge system integrity constraint rules are established, and based on these rules, the knowledge element pairs involved in the relationships and their logical dependencies are set as prohibited adjustment areas.

[0052] After constructing the knowledge transfer path, it is necessary to ensure that adjustments to the scheme do not disrupt the inherent logical structure of the knowledge system. A local association network is established, centered on necessary transfer nodes, to guarantee the integrity of knowledge transfer. Specifically, for each identified necessary transfer node, the preceding and succeeding knowledge elements directly related to that node are located by tracing its knowledge source and flow. A preceding knowledge element refers to a knowledge unit that precedes the necessary transfer node in the knowledge transfer sequence and has a direct semantic dependency or logical derivation relationship with that node; a succeeding knowledge element is knowledge content further developed or extended based on the necessary transfer node. Using the necessary transfer node as the central node, all identified preceding and succeeding knowledge elements are treated as adjacent nodes to construct a local association network. This local association network is represented by a directed graph structure, where nodes represent knowledge elements, edges represent the relationships between knowledge elements, and the direction of the edges indicates the flow of knowledge transfer. In a typical local association network, if a necessary transit node is concept C, its predecessor knowledge elements include basic concept A and definition description B, and its successor knowledge elements include application example D and inference conclusion E, then the local association network contains directed association edges from A to C, from B to C, from C to D, and from C to E.

[0053] After constructing a local association network, a deductive dependency analysis is performed on the relationships within the network to identify key relationships that support the continuity of the transmission path. The deductive dependency analysis employs a method for assessing the impact of missing relationships. Specifically, each association edge in the local association network is hypothetically deleted or modified to simulate scenarios where the relationship is missing or altered. The impact of this operation on the overall logical integrity of the transmission path is then assessed. The degree of impairment to logical integrity is quantified by a transmission path interruption index, which comprehensively considers the number of knowledge reasoning chain breaks caused by missing relationships, the decline in the comprehensibility of subsequent knowledge elements, and the degree of disruption to the knowledge transmission loop. Taking a local association network containing a concept derivation sequence as an example, if the derivation relationship from premise knowledge element P to conclusion knowledge element Q is deleted, and this deletion prevents the reader from directly deriving Q from P, and there is no alternative derivation path in the network, then the missing relationship is considered to have caused a transmission path interruption, and the degree of impairment to logical integrity is high. Conversely, if deleting a supplementary explanatory relationship, while reducing content richness, does not affect the continuity of the core knowledge reasoning chain, then the degree of impairment to logical integrity is low. By traversing all the edges in the local association network, the transmission path interruption index corresponding to the absence of each edge is calculated. Associations with interruption indices exceeding a preset threshold are identified as key associations that support the continuity of the transmission path. This threshold is dynamically set based on the professional field of the published content and the knowledge background of the target readership. For academic monographs, the threshold is set lower to retain more details of logical derivation; for popular science books, the threshold can be appropriately increased to simplify the knowledge transmission chain.

[0054] After identifying key relationships, integrity constraint rules for the knowledge system are established based on these relationships. These constraint rules are represented in triplicate form, recording the preceding knowledge elements, relationship types, and subsequent knowledge elements involved in the key relationships. Relationship types include definition-dependent, derivation-supported, concept-extended, and instance-verified types. Definition-dependent relationships indicate that understanding subsequent knowledge elements depends on the definitions or conceptual boundaries provided by preceding knowledge elements; derivation-supported relationships indicate that subsequent knowledge elements are conclusions derived from preceding knowledge elements through logical reasoning or mathematical calculation; concept-extended relationships indicate that subsequent knowledge elements expand or deepen the conceptual scope of preceding knowledge elements; and instance-verified relationships indicate that subsequent knowledge elements verify or explain the theoretical expositions of preceding knowledge elements through specific cases or experimental data. When establishing constraint rules, not only the relationships themselves are recorded, but also their position index and importance weight within the overall knowledge transfer path are marked. The location index is used to trace the logical level of the relationship in the knowledge expansion sequence; the importance weight is quantified based on the contribution of the relationship to the integrity of the overall knowledge system, and the contribution is calculated by analyzing the number and scope of downstream knowledge elements affected by deleting the relationship.

[0055] Based on the established knowledge system integrity constraint rules, the knowledge element pairs involved in the rules and their logical dependencies are set as prohibited adjustment areas. The setting of prohibited adjustment areas is implemented using a region boundary labeling mechanism. Specifically, for each key relationship recorded in the constraint rules, the preceding and subsequent knowledge elements connected by this relationship are extracted, and this knowledge element pair is used as the basic unit for prohibited adjustments. At the semantic annotation layer of the published content text, a prohibited modification marker is added to the knowledge element pair. This marker includes three types of constraint instructions: prohibited deletion, prohibited replacement, and prohibited related modification. The prohibited deletion constraint ensures that no knowledge element in the knowledge element pair will be removed during content simplification; the prohibited replacement constraint prevents the description of a knowledge element from being replaced by irrelevant or weakly related content; and the prohibited related modification constraint ensures that the type and direction of the relationship between knowledge element pairs are not changed. In addition to the knowledge element pair itself, auxiliary content supporting the relationship must also be included in the prohibited area. Auxiliary content includes explanatory descriptions of the relationship, transitional connecting statements, and descriptions of intermediate steps supporting logical deduction. Although these supplementary contents are not directly used as knowledge elements, they play an important role in understanding and maintaining the logical rationality of relationships, and therefore also need to be protected by adjustment constraints.

[0056] In practical applications, adjusting the setting of prohibited regions requires considering the overlapping and nesting relationships between regions. When the local association networks of multiple necessary transit nodes intersect, shared knowledge elements or associations may simultaneously belong to multiple prohibited regions. For such overlapping regions, a constraint strength superposition strategy is adopted, meaning that the region is jointly protected by all relevant constraint rules, and the restrictions on deletion or modification are more stringent. Nesting relationships occur when an association itself contains sub-level logical dependencies. For example, a complex derivation relationship consists of multiple basic reasoning steps. In this case, the entire derivation chain and each reasoning step within it need to be included in the prohibited regions, forming a multi-level nested constraint structure. By establishing a hierarchical mapping table for adjusting prohibited regions, the inclusion and dependency relationships between different prohibited regions are recorded. When executing a content adjustment scheme, the adjustment operation engine will first retrieve this mapping table to ensure that any modification does not touch the boundaries of the prohibited regions.

[0057] To enhance the adaptability of constraint rules, limited relaxation of prohibited regions is permitted under specific conditions. When the density optimization decision mechanism identifies redundant expressions or repetitive statements in a knowledge element within a prohibited region, and this redundancy does not affect the logical integrity of the relationships, the expression of that knowledge element can be simplified and optimized, but its core semantic content and related attributes must remain unchanged. Such limited relaxation operations require review by the constraint rule verification module, which automatically assesses the potential impact of the simplification operation on the continuity of the transmission path and approves execution only if the impact assessment result is below a safe threshold. Simultaneously, all adjustments that undergo limited relaxation are recorded in the adjustment log, including a comparison of the content before and after the adjustment, impact assessment parameters, and the basis for the review decision, providing a traceable record of operations for subsequent model iteration optimization and quality review.

[0058] By establishing rules to constrain the integrity of the knowledge system and setting prohibited areas for adjustment, it is ensured that the content adjustment plan will not damage the knowledge transmission logic and theoretical system integrity of the published content while optimizing the distribution of knowledge density, thereby achieving a balance between content simplification and knowledge integrity.

[0059] A second aspect of this invention provides a publishing content analysis and quality assessment system based on natural language processing, comprising: The knowledge extraction unit is used to acquire the published content text to be analyzed, divide the published content text into semantic units, and extract the knowledge elements and their relationships in each semantic unit. A density representation unit is used to construct a multi-dimensional knowledge density representation model. The multi-dimensional knowledge density representation model performs coupled calculations by measuring the independence of knowledge elements within a semantic unit and the tightness of the relationship between knowledge elements, generating a density representation result that reflects the knowledge carrying efficiency. The optimization decision unit is used to perform density distribution analysis on the published content text based on the density characterization results, identify abnormal patterns in the density distribution, establish a density optimization decision mechanism, generate a content adjustment scheme under the constraint of maintaining the integrity of the knowledge system based on the change trend of the density gradient in the abnormal pattern and the transmission path of the relationship between the knowledge elements, and ensure the traceability of the adjusted knowledge link through the continuity verification of the transmission path. The iterative adjustment unit is used to execute the content adjustment scheme and obtain feedback data on the adjustment effect, and to use the feedback data to iteratively optimize the independence measurement calculation rules and the tightness measurement calculation rules in the multi-dimensional knowledge density representation model.

[0060] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0061] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0062] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing and assessing the quality of published content based on natural language processing, characterized in that: include: Obtain the published content text to be analyzed, divide the published content text into semantic units, and extract the knowledge elements and their relationships in each semantic unit; A multi-dimensional knowledge density representation model is constructed. The multi-dimensional knowledge density representation model performs coupled calculations by measuring the independence of knowledge elements within a semantic unit and the tightness of the relationship between knowledge elements, thereby generating a density representation result that reflects the knowledge carrying efficiency. Based on the density characterization results, density distribution analysis is performed on the published content text to identify abnormal patterns in density distribution, establish a density optimization decision mechanism, generate a content adjustment scheme under the constraint of maintaining the integrity of the knowledge system based on the changing trend of density gradient in the abnormal patterns and the transmission path of the relationship between knowledge elements, and ensure the traceability of the adjusted knowledge link through the continuity verification of the transmission path. The content adjustment scheme is executed and feedback data on the adjustment effect is obtained. The feedback data is then used to iteratively optimize the independence measurement calculation rules and the tightness measurement calculation rules in the multi-dimensional knowledge density representation model.

2. The method of claim 1, wherein, A multi-dimensional knowledge density representation model is constructed. This model couples the independence measure of knowledge elements within semantic units with the tightness measure of the relationship between knowledge elements to generate a density representation result that reflects the knowledge carrying efficiency, including: For each semantic unit, the independence measure of each knowledge element within it is calculated. The independence measure quantifies the comprehensibility of the knowledge element in the absence of context by assessing the degree of dependence of the knowledge element on the external context of the semantic unit. The tightness measure of the relationship between knowledge elements within the semantic unit is calculated. The tightness measure quantifies the coupling strength between knowledge elements by evaluating the necessity of logical deduction between knowledge elements and the directness of the deduction path. The independence measure is used as the basic contribution weight of knowledge elements, and the tightness measure is used as the synergistic gain coefficient between knowledge elements. Through the combination operation of the basic contribution weight and the synergistic gain coefficient, a density value representing the effective knowledge carrying capacity within a unit text length is generated. For all semantic units in the published content text, the density value of each semantic unit is calculated, and the density value is associated and mapped with the position information and hierarchical information of the semantic unit to form a density representation result that includes spatial distribution features and hierarchical distribution features.

3. The method of claim 2, wherein, The density value representing the effective knowledge carrying capacity per unit text length is generated by combining the basic contribution weight and the collaborative gain coefficient, including: For each knowledge element within a semantic unit, extract the basic contribution weight of that knowledge element and the collaborative gain coefficient between that knowledge element and its associated knowledge elements. A weight gain coupling calculation expression is established. Based on the weight gain coupling calculation expression, the basic contribution weight is used as the initial contribution component, and the product of the collaborative gain coefficient and the basic contribution weight of the associated knowledge element is used as the collaborative contribution component. The comprehensive contribution value of the knowledge element is obtained by weighted summation of the initial contribution component and the collaborative contribution component. The total knowledge carrying capacity of the semantic unit is obtained by summing the comprehensive contribution values ​​of all knowledge elements within the semantic unit. The total knowledge carrying capacity is adjusted by applying a marginal decrease correction. By constructing a decay function that reflects the impact of knowledge element density on carrying efficiency, and based on the distribution characteristics of the number of knowledge elements in the semantic unit and the cooperative gain coefficient, the total knowledge carrying capacity is nonlinearly adjusted to generate the corrected total knowledge carrying capacity. Divide the total amount of knowledge carried by the modified semantic unit by the text length to obtain the density value.

4. The method of claim 1, wherein, Based on the density characterization results, density distribution analysis is performed on the published content text to identify abnormal patterns in the density distribution and establish a density optimization decision-making mechanism, including: Based on the density values ​​of each semantic unit in the density characterization results, a density distribution map reflecting the spatial and hierarchical dimensions of the published content is constructed. Morphological features are extracted from the density distribution map, and abnormal patterns in the density distribution are identified by detecting local extrema and gradient abrupt change points in the density values. By tracing the knowledge element relationships of semantic units within the region corresponding to the abnormal pattern, analyzing the breakage and clustering characteristics of the knowledge element relationships, determining the knowledge structural root cause of the abnormal pattern, and establishing a density optimization decision mechanism.

5. The method of claim 1, wherein, Based on the transmission path of the density gradient change trend and the relationship between knowledge elements in the aforementioned abnormal patterns, a content adjustment scheme is generated under the constraint of maintaining the integrity of the knowledge system, including: For the aforementioned abnormal pattern, the density gradient change trend at the boundary of the abnormal region is extracted, and based on the density gradient change trend, the target semantic unit that needs to be density adjusted and its adjustment target direction are determined. Tracing the relationship transmission path of knowledge elements within the target semantic unit, and extracting necessary transmission nodes in the logical deduction chain by identifying the logical deduction chain between the starting knowledge element and the ending knowledge element in the relationship transmission path; Establish knowledge system integrity constraint rules, and set the association relationship between the necessary transmission node and its adjacent knowledge elements as an adjustment prohibited area according to the knowledge system integrity constraint rules; Based on the prohibited adjustment regions, and under the constraints of the knowledge system integrity rules, a content adjustment scheme is generated for the target semantic unit.

6. The method of claim 5, wherein, Establish knowledge system integrity constraint rules, and set the association relationship between the necessary transmission node and its adjacent knowledge elements as an adjustment prohibited area according to the knowledge system integrity constraint rules, including: For the necessary transmission node, identify the predecessor and successor knowledge elements directly associated with the necessary transmission node, and construct a local association network centered on the necessary transmission node; Deductive dependency analysis is performed on the relationships in the local association network. By evaluating the degree of damage to the logical integrity of the transmission path after deleting or modifying a certain relationship, the relationships that support the continuity of the transmission path are identified. Based on the aforementioned relationships, knowledge system integrity constraint rules are established, and based on these rules, the knowledge element pairs involved in the relationships and their logical dependencies are set as prohibited adjustment areas.

7. A system for natural language processing based analysis and quality assessment of published content for implementing the method according to any one of claims 1 to 6, characterized in that, include: The knowledge extraction unit is used to acquire the published content text to be analyzed, divide the published content text into semantic units, and extract the knowledge elements and their relationships in each semantic unit. A density representation unit is used to construct a multi-dimensional knowledge density representation model. The multi-dimensional knowledge density representation model performs coupled calculations by measuring the independence of knowledge elements within a semantic unit and the tightness of the relationship between knowledge elements, generating a density representation result that reflects the knowledge carrying efficiency. The optimization decision unit is used to perform density distribution analysis on the published content text based on the density characterization results, identify abnormal patterns in the density distribution, establish a density optimization decision mechanism, generate a content adjustment scheme under the constraint of maintaining the integrity of the knowledge system based on the change trend of the density gradient in the abnormal pattern and the transmission path of the relationship between the knowledge elements, and ensure the traceability of the adjusted knowledge link through the continuity verification of the transmission path. The iterative adjustment unit is used to execute the content adjustment scheme and obtain feedback data on the adjustment effect, and to use the feedback data to iteratively optimize the independence measurement calculation rules and the tightness measurement calculation rules in the multi-dimensional knowledge density representation model.

8. An electronic device, comprising: include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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