Intelligent writing support method and system based on semantic map

Abstract

The invention discloses an intelligent writing support method and system based on a semantic map, and relates to the technical field of intelligent writing assistance. The intelligent writing support method comprises the following steps: initializing writing, determining a writing intention and a context, generating an initial context feature vector, constructing a basic semantic map model containing nodes and relation weights, and inputting the basic semantic map model into a real-time monitoring module for subsequent monitoring and adjustment. The real-time monitoring module analyzes user input, dynamically evaluates writing intention and context change, generates an intention-context state vector, transmits the intention-context state vector to the semantic map dynamic processing module, triggers semantic map dynamic expansion and updating, inputs an updated model into the writing suggestion generation module, generates personalized suggestions, and sends the personalized suggestions to the writing suggestion generation module; the model is optimized by using a graph neural network algorithm and then is output to the writing suggestion generation module, and the writing suggestion generation module looks for knowledge paths and determines writing thoughts and expansion directions by using the updated model, and presents writing suggestions in a visual mode to assist writing in combination with audience information adaptive styles and expression modes.

Description

Technical Field

[0001] The present invention relates to the field of intelligent writing assistance technology, and in particular to an intelligent writing support method and system based on semantic graphs. Background Art

[0002] With the development of natural language processing technology, intelligent writing support systems are gradually emerging. These systems utilize technologies such as semantic understanding and knowledge graphs to provide users with richer writing assistance functions. However, current intelligent writing support technology is still in its developmental stages, primarily focusing on basic grammar checking and simple writing suggestions.

[0003] Existing intelligent writing support systems suffer from numerous shortcomings. First, their semantic graph construction and update capabilities are limited, making it difficult to dynamically adjust to user intent and context in real time. This results in writing suggestions being disconnected from actual user needs. Second, they are unable to deeply mine user intent and contextual information, resulting in a lack of targeted and personalized writing suggestions. Third, semantic graph optimization algorithms are inefficient and struggle to process large amounts of graph data, impacting the real-time and accuracy of writing support. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent writing support method based on semantic graphs to solve the problems of existing intelligent writing support methods, such as insufficient dynamic adjustment of semantic graphs, insufficient understanding of writing intentions and contexts, and how to efficiently optimize semantic graphs and generate personalized writing suggestions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides an intelligent writing support method based on semantic graph, which is characterized by comprising the following steps:

[0008] When writing begins, the system analyzes the initial writing information and user preferences, determines the writing intention and context, generates an initial context feature vector, and builds a basic semantic graph model with node and relationship weights based on this. At the same time, the model is input into the real-time monitoring module for subsequent writing monitoring and dynamic adjustment of the semantic graph.

[0009] The real-time monitoring module analyzes user input during the writing process, dynamically evaluates writing intentions and context changes, generates an intention-context state vector and passes it to the semantic graph dynamic processing module, triggering the dynamic expansion and updating of the semantic graph. At the same time, the updated semantic graph model is input into the writing suggestion generation module to generate personalized writing suggestions.

[0010] After receiving the intention-context state vector, the semantic graph dynamic processing module adds relevant nodes according to the changes in writing intention, updates the semantic relationships and weights based on the context state, and uses the graph neural network algorithm to optimize the semantic graph model to maintain consistency and coherence, and then outputs the model to the writing suggestion generation module.

[0011] The writing suggestion generation module uses the updated semantic graph model to find the knowledge path associated with the current writing node, determine the writing ideas and expansion direction, adapt the style and expression method based on the audience information, and finally present the generated writing suggestion content to the user in a visual way to assist writing.

[0012] As a preferred solution of the intelligent writing support method based on semantic graphs described in the present invention, the system adopts a deep learning algorithm to extract the user's writing history features when analyzing the initial writing information and user preferences, dynamically adjusts the initial context feature vector in combination with real-time network data, and integrates user preferences and real-time trend information through feature fusion technology, optimizes the basic semantic graph model construction process, and enhances the model's responsiveness to hot topics.

[0013] As a preferred solution of the intelligent writing support method based on semantic graph described in the present invention, the real-time monitoring module focuses on the key semantic units of the text through the attention mechanism when analyzing user input, and introduces writing rhythm parameters at the same time, judges the degree of writing difficulty according to the user input speed and pause time, dynamically adjusts the intention-context state vector generation strategy, and combines the context semantic consistency test to improve the vector transmission accuracy and optimize the dynamic expansion trigger mechanism of the semantic graph.

[0014] As a preferred solution of the intelligent writing support method based on semantic graphs described in the present invention, the semantic graph dynamic processing module calls the preset concept association database to quickly locate high-value concept nodes when adding related nodes, and uses the domain expert knowledge base rules to verify the rationality of the update when updating semantic relationships based on the context status. Through the graph structure consistency verification algorithm, the consistency and accuracy of the semantic graph model are ensured, thereby optimizing the basis for generating writing suggestions.

[0015] As a preferred solution of the semantic graph-based intelligent writing support method described in the present invention, the writing suggestion generation module adopts a multi-strategy path search algorithm to comprehensively evaluate path factors when searching for knowledge paths, screens the optimal knowledge path combination, and constructs an audience preference model to analyze the characteristics of different audience groups, thereby achieving personalized adaptation of writing suggestion styles and expressions, and improving the accuracy of suggestions.

[0016] As a preferred solution of the intelligent writing support method based on semantic graphs described in the present invention, the semantic graph dynamic processing module introduces quantum computing parallel processing technology to accelerate large-scale data operations when optimizing the semantic graph model, and designs an adaptive graph neural network architecture to automatically adjust network parameters according to dynamic changes in the graph, thereby improving model optimization efficiency and ensuring real-time writing support.

[0017] As a preferred solution of the semantic graph-based intelligent writing support method described in the present invention, when presenting writing suggestions, the writing suggestion generation module develops an interactive visualization tool to allow users to directly browse the graph content, supports users to provide instant feedback on writing suggestions, and the system corrects the graph model and suggestion generation strategy in real time according to the feedback, forming a closed-loop optimization mechanism to continuously improve the writing support effect.

[0018] The beneficial effects of the present invention are as follows: through the real-time dynamic construction and updating of the semantic graph, writing support is closely aligned with user intentions and context changes, avoiding the problem of disconnected writing suggestions in traditional systems. The system uses deep learning algorithms to extract user preferences and optimizes context feature vectors in combination with network trends, which can accurately capture hot topics and incorporate them into writing suggestions. The combination of the attention mechanism and the writing rhythm parameters improves the ability to identify writing difficulties and makes the expansion of the semantic graph more accurate. The call to the preset concept association database and the domain expert knowledge base ensures the rationality of node addition and semantic relationship update, and enhances the scientific nature and accuracy of writing suggestions. The application of multi-strategy path search algorithms and audience preference models realizes personalized adaptation and precise presentation of writing suggestions. The introduction of quantum computing and adaptive rapid stream neural network architecture improves the efficiency of model optimization and ensures the real-time nature of writing support. The combination of interactive visualization tools and user feedback mechanisms forms a closed-loop optimization mechanism to continuously improve the writing support effect. The present invention improves the performance and user experience of the intelligent writing support system from multiple dimensions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a flowchart of the intelligent writing support method based on semantic graph in Example 1. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0024] Reference Figure 1 , is an embodiment of the present invention, which provides an intelligent writing support method based on semantic graph, characterized by comprising the following steps:

[0025] When writing begins, the system analyzes the initial writing information and user preferences, determines the writing intention and context, generates an initial context feature vector, and builds a basic semantic graph model with node and relationship weights based on this. At the same time, the model is input into the real-time monitoring module for subsequent writing monitoring and dynamic adjustment of the semantic graph.

[0026] The real-time monitoring module analyzes user input during the writing process, dynamically evaluates writing intentions and context changes, generates an intention-context state vector and passes it to the semantic graph dynamic processing module, triggering the dynamic expansion and updating of the semantic graph. At the same time, the updated semantic graph model is input into the writing suggestion generation module to generate personalized writing suggestions.

[0027] After receiving the intention-context state vector, the semantic graph dynamic processing module adds relevant nodes according to the changes in writing intention, updates the semantic relationships and weights based on the context state, and uses the graph neural network algorithm to optimize the semantic graph model to maintain consistency and coherence, and then outputs the model to the writing suggestion generation module.

[0028] The writing suggestion generation module uses the updated semantic graph model to find the knowledge path associated with the current writing node, determine the writing ideas and expansion direction, adapt the style and expression method based on the audience information, and finally present the generated writing suggestion content to the user in a visual way to assist writing.

[0029] It should be noted that at the beginning of writing, the system simultaneously performs two key tasks. First, it collects and deeply analyzes the user's initial writing input in real time, covering textual details such as the title, keywords, and first paragraph. Second, it comprehensively reviews the user's historical writing history to accurately extract user preferences, using them as key parameters to assist in analysis. Using complex algorithms, the system extracts writing intent types, which can be subdivided into common categories such as narrative, description, argumentation, and explanation. It also accurately identifies the contextual information of the current writing, including core elements such as genre, theme, and audience. Based on these key elements, the system uses specialized encoding techniques to generate an initial context feature vector. This vector, in the form of high-dimensional data, comprehensively and accurately represents the complex characteristics of the current writing context. The system then accurately retrieves and extracts nodes and relationships from a pre-existing general semantic graph knowledge base that closely match the initial context feature vector. These nodes include core concept nodes and entity nodes, while relationships reflect their semantic connections. Following pre-set weighting rules, the system assigns appropriate weights to each node and relationship, constructing a complete basic semantic graph model. Finally, the model is stably transferred to the real-time monitoring module, building a solid initial framework for subsequent writing monitoring and dynamic adjustment of the semantic graph.

[0030] This step relies on a dual analysis of the initial writing information and user preferences to accurately establish the writing intention and context, and convert it into the initial context feature vector to build a basic semantic graph model that meets the needs. This model is like a precise starting point in writing navigation, providing a highly targeted knowledge framework for subsequent writing and ensuring the precise start of writing support. At the same time, timely input of the model into the real-time monitoring module is equivalent to laying a "starting track" for dynamic monitoring throughout the writing process, ensuring that the system can always capture changes in writing, providing a reliable foundation for the subsequent dynamic adjustment of the semantic graph, greatly improving the real-time and consistency of writing support, and effectively avoiding the disadvantages of writing support being out of touch with user needs.

[0031] While users are fully engaged in writing, the real-time monitoring module remains on standby. Whenever a user enters new text, the module immediately activates its natural language processing mechanism, precisely breaking down the text and extracting key semantic units such as keywords, phrases, and syntactic structures. Simultaneously, it utilizes pre-trained writing intent recognition and context awareness models, conducting in-depth comparative analysis using the initial context feature vector as a reference. The module rapidly responds to subtle shifts in the input's implicit writing intent, such as a shift from narrative to argumentative, or updates to contextual information, such as the introduction of a new research branch in academic writing. Using sophisticated algorithms, it generates an intent-context state vector in real time. This vector, in the form of dynamic data, reflects the current writing intent and contextual environment. This vector is then seamlessly transferred to the semantic graph dynamic processing module, activating its dynamic expansion and update mechanism. While these operations proceed in parallel, the real-time monitoring module also continuously optimizes the semantic graph model, providing it with a stable and timely feed to the writing suggestion generation module, providing the latest and most dynamic knowledge "fuel" for the creation of personalized writing suggestions.

[0032] This step gives the writing support system a high degree of dynamic adaptability. The real-time monitoring module, with its sensitive semantic capture and analysis capabilities, can accurately perceive the real-time changes in writing intentions and contexts, generate intention-context state vectors, and provide precise guidance for subsequent operations. This dynamic transmission mechanism enables the semantic graph to be instantly expanded and updated, just like installing a smart "knowledge tentacles" for the writing support system, so that it can always keep up with the changes in the user's writing ideas. The updated semantic graph model is quickly input into the writing suggestion generation module, which provides accurate and timely knowledge support for the output of personalized writing suggestions, ensuring that the suggestions can closely fit the user's actual current writing scenario, greatly enhancing the effectiveness and practicality of writing suggestions, and effectively breaking the limitation of traditional writing support systems that are difficult to adapt to dynamic writing needs.

[0033] Upon receiving the intent-context state vector, the semantic graph dynamic processing module first utilizes a signal parsing unit to deeply decompose the vector, accurately extracting the details of the writing intent's shifts and the updated context state. Based on the evolving nature of the writing intent, the module rapidly activates a node search process, accurately locating concept and entity nodes that are closely related to the new intent within the vast semantic knowledge base. For example, when writing enters the argumentative dimension, key nodes such as "argument construction," "evidence screening," and "argument execution" are immediately captured and sequentially embedded into the existing semantic graph architecture, injecting new intellectual vitality into the graph. Simultaneously, in conjunction with context state updates, the module initiates a mode for adjusting semantic relationships and weights. In the context of a rigorous academic paper, this module prioritizes strengthening the weighted relationship between "data support" and "theoretical derivation," and appropriately supplementing the links between "cutting-edge academic viewpoints" that "corroborate" and "refute" each other. After completing node expansion and relationship adjustments, the module fully activates its graph neural network algorithm, deeply and iteratively computing embedded representations of graph nodes and relationships. This intelligently optimizes the overall graph architecture, removes redundant connections, strengthens key links, and ensures the consistency and coherence of the semantic graph's knowledge logic. Finally, the refined and optimized semantic graph model is precisely output to the writing suggestion generation module, preparing a sophisticated "knowledge mold" for generating writing suggestions.

[0034] This step realizes the precise dynamic shaping of the semantic graph. Based on the in-depth analysis of the intent-context state vector, new nodes are added, semantic relationships and weights are updated in a timely and accurate manner, so that the semantic graph can be like a flexible knowledge carrier, adapting to the extension of the user's writing ideas in real time. The deep optimization of the graph neural network algorithm is like equipping the semantic graph with a set of "intelligent shaping systems", which comprehensively carves the graph structure to ensure that it presents the knowledge context in a highly consistent and coherent logical form, providing a pure and high-quality knowledge base for the subsequent generation of writing suggestions. This effectively ensures that the knowledge content and logical structure of the writing suggestions can accurately match the user's writing process, greatly improves the professionalism and reliability of writing support, and effectively avoids the potential erosion of the quality of writing suggestions by defects in the quality of the semantic graph.

[0035] After the writing suggestion generation module collects the updated semantic graph model, it immediately initiates a path search process. Based on pre-set multi-dimensional evaluation criteria, including the semantic coherence, information richness, and logical depth of the knowledge path, the program accurately locates the knowledge path closely connected to the current writing point within the complex network of nodes and relationships in the semantic graph. For example, in the case of a technology product review, this path might logically extend from "product design analysis" to "product performance data," and then to "comprehensive user experience feedback." Following this knowledge path, the module intelligently derives the continuation and expansion of the current writing process, providing users with high-quality writing guidance. Furthermore, the module integrates an audience analysis unit to deeply analyze the target audience's characteristics, covering dimensions such as their knowledge base, interests, preferences, and reading habits. If writing for professionals, professional terminology and rigorous sentence structure are recommended; if the audience is the general public, easy-to-understand and vivid expression is recommended. Finally, the visual rendering engine is called to convert the writing suggestions into an intuitive and easy-to-read visual form, or it is displayed in the sidebar of the writing software in a staggered manner as prompt cards, or cleverly embedded in the text editing area as smart pop-ups, and delivered accurately to users to help advance writing throughout the process.

[0036] With this sophisticated process, the writing suggestion generation module successfully achieves precise customization and efficient presentation of writing suggestions. By exploring knowledge paths and anchoring writing ideas, it provides users with accurate and logical writing guidance, avoiding train of thought blockages and logical confusion mid-writing. The deep integration of audience information ensures that the style and expression of writing suggestions precisely match the expectations of the target audience, comprehensively enhancing the appeal and appeal of the written content. The visual presentation method greatly optimizes the readability and ease of use of the suggestions. Users can obtain writing inspiration and assistance with a single click without tedious operations, significantly enhancing the user experience of the writing support system. This is like creating a personal writing think tank for users, accompanying them throughout the writing process, effectively improving the quality of the final writing, and fully promoting the efficient operation of the writing process.

[0037] Specifically, when analyzing initial writing information and user preferences, the system uses a deep learning algorithm to extract user writing history features, dynamically adjusts the initial context feature vector in combination with real-time network data, and integrates user preferences and real-time trend information through feature fusion technology, optimizing the basic semantic graph model construction process and enhancing the model's responsiveness to hot topics.

[0038] It should be noted that when the system initiates the analysis of initial writing information and user preferences, it first invokes a deep learning algorithm module. This module takes the user's past writing as input and, through computational processing within a multi-layer neural network structure, extracts a vector representation of key features such as the user's writing style, frequently used vocabulary, and thematic tendencies. This is known as the user's writing history feature vector. Simultaneously, the system activates a real-time network data monitoring port to collect real-time trend information, such as popular hot topics and high-frequency keywords, and converts this information into a structured data vector. Subsequently, using feature fusion technology, the user preference feature vector, writing history feature vector, and real-time trend information vector are combined linearly or nonlinearly. Specifically, this is accomplished by constructing a fusion matrix, with each vector serving as a row or column element. Through operations such as matrix multiplication and weighted summation, a new composite feature vector is generated. This vector retains the user's personalized writing characteristics while incorporating current hot topics and trends. Finally, this composite feature vector is fed into the algorithm for constructing a basic semantic graph model. The model's node generation rules and relationship weight assignment rules are adjusted to ensure that the constructed basic semantic graph model not only encompasses the user's personalized knowledge structure but also responds sensitively to hot topics.

[0039] By using deep learning algorithms to mine the historical features of users' writing, the system achieves a deep understanding of users' writing styles and habits, which is equivalent to customizing a key to unlock personalized writing support for each user. Real-time collection of network data and integration into analysis is like installing sensitive tentacles for the writing support system, enabling it to accurately capture current hot spots. The process of building a basic semantic graph model after integration with feature fusion technology breaks the static and single limitations of traditional models. The constructed model can not only show the user's unique writing characteristics, but also resonate with contemporary topics. In writing support, this process is like building a knowledge stage for users that combines personality and trends, effectively inspiring users' writing inspiration, improving the timeliness and attractiveness of writing content, making writing suggestions more in line with user needs, greatly enhancing the intelligence level and practicality of the writing support system, and strongly promoting the development of personalized intelligent writing support technology.

[0040] Specifically, when analyzing user input, the real-time monitoring module focuses on key semantic units of the text through the attention mechanism, and at the same time introduces writing rhythm parameters to judge the degree of writing difficulty based on the user's input speed and pause time, dynamically adjusts the intention-context state vector generation strategy, and combines context semantic consistency verification to improve vector transmission accuracy and optimize the dynamic expansion trigger mechanism of the semantic graph.

[0041] It should be noted that the real-time monitoring module receives the user's input text data stream in real time during the writing process. First, the attention mechanism module is activated, scanning and analyzing the input text word by word and sentence by sentence, calculating the semantic weight of each word and phrase in the current text paragraph. By constructing an attention score matrix, it identifies high-weighted words and phrases as key semantic units and focuses on these units for in-depth semantic analysis. Simultaneously, the writing rhythm monitoring module operates synchronously, recording the user's input speed parameters in real time, including words per minute, phrase input intervals, and pause parameters such as pause frequency and duration. Based on these parameters, the system's built-in writing difficulty assessment model activates and, according to preset threshold rules, determines whether the current writing process is experiencing difficulty. For example, if the pause time exceeds the set average pause duration and the input speed decreases significantly, the user is considered to be experiencing a writing block. Based on the writing difficulty assessment results, the system dynamically adjusts the generation strategy of the intention-context state vector, such as changing the weight distribution of corresponding semantic dimensions in the vector or adding or removing dimensional elements in the vector. Subsequently, the initially generated intention-context state vector is checked for consistency with the contextual semantics. By constructing a semantic similarity calculation matrix, the coherence of the semantics represented by the current vector and the previous semantics is compared. If inconsistencies are found, such as semantic abruptness or logical breaks, the system will backtrack and adjust the vector generation parameters until the vector passes the consistency check. Finally, the optimized vector will be passed to subsequent modules, synchronously triggering the relevant processes of dynamic expansion of the semantic graph, and providing precise semantic guidance for writing support.

[0042] Through this series of sophisticated operations of the real-time monitoring module, first, the attention mechanism accurately focuses on the key semantic units of the text, ensuring the targeting of semantic analysis, avoiding ineffective processing of redundant information, and effectively improving the efficiency and accuracy of semantic parsing; secondly, the introduction of writing rhythm parameters and accurate assessment of the degree of writing difficulty enable the system to keenly understand the user's immediate difficulties in the writing process, just like equipping the writing support system with a "difficulty sensor", realizing the precise triggering and timely intervention of writing support; thirdly, the dynamic adjustment of the intention-context state vector generation strategy, combined with the context semantic consistency test, doubly guarantees the accuracy and reliability of vector transmission, and avoids writing support errors caused by semantic deviations. This process is like laying a precise "semantic track" for the dynamic expansion of the semantic graph, ensuring that the expansion direction of the semantic graph is highly consistent with the user's actual writing needs, greatly enhancing the intelligence level and practicality of the writing support system, and effectively promoting the in-depth development of intelligent writing support technology, making writing support more in line with the user's real writing scenarios and needs.

[0043] Specifically, when adding related nodes, the semantic graph dynamic processing module calls the preset concept association database to quickly locate high-value concept nodes. When updating semantic relationships based on context status, it uses the domain expert knowledge base rules to verify the rationality of the update. Through the graph structure consistency verification algorithm, it ensures the coherence and accuracy of the semantic graph model, optimizing the basis for generating writing suggestions.

[0044] It should be noted that the semantic graph dynamic processing module operates closely around the rules of the preset concept association database and domain expert knowledge base when performing node addition and semantic relationship update operations. First, the module receives the intention-context state vector transmitted by the real-time monitoring module and comprehensively analyzes the vector, accurately extracting the key information of the writing intention change and the core content of the context state update. Subsequently, the module quickly calls the preset concept association database. From the massive amount of concept node information, based on the extracted key information, it quickly locates high-value concept nodes that are highly relevant to the current writing intention and accurately adds them to the existing semantic graph architecture, injecting new knowledge vitality into the graph. At the same time, the module carefully sorts out and updates the semantic relationships in the semantic graph based on the updated context state. In this process, the rules of the domain expert knowledge base are fully utilized to rigorously verify the rationality of each updated semantic relationship to ensure that the newly added relationships are rational, accurate, and authoritative. After adding nodes and updating relationships, the module further utilizes a graph structure consistency verification algorithm to conduct a comprehensive scan and in-depth analysis of the entire semantic graph model, verifying the logical connection between nodes and relationships within the graph and checking for structural loopholes or inconsistencies. Through a complex series of calculations and verification processes, the module ensures the high degree of coherence and accuracy of the semantic graph model's overall architecture, laying a solid foundation for the subsequent generation of writing suggestions, providing high-quality, credible knowledge support, and enhancing the scientific and practical nature of writing suggestions.

[0045] Through this series of operational steps in the semantic graph dynamic processing module, the semantic graph model is precisely expanded and optimized. The use of a pre-set concept association database equips the module with an efficient knowledge location tool, enabling it to quickly and accurately locate high-value concept nodes related to writing intent, effectively enriching the knowledge content of the semantic graph. The application of domain expert knowledge base rules is like installing a precise "semantic ruler" for the module, ensuring the rigor and reliability of newly added semantic relationships and avoiding the introduction of knowledge errors or unreasonable relationships. The implementation of the graph structure consistency verification algorithm is like a comprehensive "health check" of the semantic graph, comprehensively ensuring the graph's coherence and accuracy, making it a well-structured and logically rigorous knowledge system. This process provides a high-quality, highly reliable knowledge foundation for the generation of writing suggestions, enabling writing suggestions to be generated based on accurate and coherent knowledge logic. This effectively improves the quality and practicality of writing suggestions, better meets users' demand for precise and scientific writing assistance tools, and promotes the development of intelligent writing support technology to a higher level.

[0046] Specifically, when searching for knowledge paths, the writing suggestion generation module uses a multi-strategy path search algorithm to comprehensively evaluate path factors, screen the optimal knowledge path combination, and construct an audience preference model to analyze the characteristics of different audience groups, thereby achieving personalized adaptation of writing suggestion styles and expressions and improving the accuracy of suggestions.

[0047] It should be noted that when the writing suggestion generation module initiates the writing suggestion generation process, it first loads the updated semantic graph model and converts it into a graph-structured data object consisting of a set of nodes and edges. Nodes represent concepts or entities, and edges represent semantic relationships. It then initializes a multi-strategy path search algorithm, setting multiple factors for evaluating paths, including path length, node weights, relationship types, and relevance to the writing topic. Starting from the current writing node, the algorithm conducts a depth-first and breadth-first search within the semantic graph, incorporating a heuristic search strategy to calculate a comprehensive evaluation score for each possible path. After multiple rounds of search and evaluation, the optimal knowledge path combination is selected as the several knowledge paths with the highest comprehensive evaluation scores. Simultaneously, the module invokes the audience analysis submodule to collect and organize characteristic data for different audience groups, such as age distribution, education level, occupation, interests, and reading habits, to construct a multi-dimensional vector of audience characteristics. Based on these vectors, a machine learning algorithm is used to train and construct an audience preference model, which outputs a probability distribution of preferences for various writing styles and expressions among different audience groups. Finally, by combining the optimal knowledge path combination with the audience preference model analysis results, personalized writing suggestions are generated, including recommended writing idea expansion directions, style types, expressions, etc., and these suggestions are organized into a structured data format and prepared to be presented to users.

[0048] Through this series of operations in the writing suggestion generation module, first, a multi-strategy path search algorithm is used to comprehensively evaluate path factors and select the optimal knowledge path combination. This is like accurately locating the best forward route in the knowledge maze of the semantic map, ensuring the logical coherence and content richness of the writing suggestions, and providing users with personalized and high-quality writing guidance. Secondly, an audience preference model is constructed and the characteristics of different audience groups are analyzed, so that the writing suggestions can be accurately matched to the reading expectations of the target audience. It is like finding the most appropriate expression "channel" for the writing content, greatly enhancing the appeal and dissemination of the writing content. This process ultimately achieves personalized adaptation and precise push of writing suggestions, effectively improving the quality and practicality of writing suggestions, helping users create works that better meet the needs of the target audience, and effectively promoting the development of intelligent writing support technology towards deep personalization and precision, meeting the urgent need for diversified and targeted writing assistance tools in modern writing scenarios.

[0049] Specifically, when optimizing the semantic graph model, the semantic graph dynamic processing module introduces quantum computing parallel processing technology to accelerate large-scale data operations, and designs an adaptive graph neural network architecture to automatically adjust network parameters according to dynamic changes in the graph, thereby improving model optimization efficiency and ensuring real-time writing support.

[0050] It should be noted that when performing model optimization tasks, the semantic graph dynamic processing module first quantizes the data structure of the semantic graph model and converts it into a quantum bit format suitable for quantum computing. Next, it activates the quantum computing parallel processing unit, leveraging the superposition and entanglement properties of quantum bits to perform parallel operations on large-scale graph data. It also simultaneously launches multiple quantum parallel computing threads, each responsible for processing a subset of the graph data, thereby achieving rapid processing and computational acceleration of the graph data. Simultaneously, the adaptive graph neural network architecture monitoring module monitors the dynamic changes of the semantic graph in real time, including the addition and deletion of nodes and the adjustment of relationship weights. Based on these changing characteristics, it applies preset network parameter adjustment rules to automatically and dynamically adjust the number of layers, number of neurons, and connection methods of the graph neural network to adapt to changes in the graph structure, improving the model's learning ability and optimization efficiency for dynamic semantic graphs and ensuring the real-time responsiveness of the writing support system.

[0051] By introducing quantum computing parallel processing technology, the semantic graph dynamic processing module can efficiently process large-scale graph data, greatly reducing the time cost of model optimization, improving the system's operating efficiency, and providing strong computational support for the real-time nature of writing support. At the same time, the design of the adaptive graph neural network architecture enables the model to automatically adjust network parameters according to the dynamic changes of the semantic graph, enhancing the model's adaptability and learning ability to graph changes, ensuring that the writing support system can respond quickly and accurately to complex and changing writing needs, providing users with more timely and effective writing assistance, effectively improving the overall performance and practicality of the writing support system, and promoting the development of intelligent writing support technology in terms of efficient processing and dynamic adaptation.

[0052] Specifically, when presenting writing suggestions, the writing suggestion generation module develops interactive visualization tools to allow users to directly browse the graph content and supports users to provide instant feedback on writing suggestions. The system corrects the graph model and suggestion generation strategy in real time based on the feedback, forming a closed-loop optimization mechanism to continuously improve the writing support effect.

[0053] It should be noted that the writing suggestion generation module simultaneously launches an interactive visualization tool when preparing to present writing suggestions to the user. This tool, based on a graphical interface, transforms the semantic graph model into an intuitive visualization. Nodes are represented by icons of varying shapes and colors, while edges are represented by lines. The thickness and style of the lines reflect the strength of the semantic relationships. Users can conveniently navigate the graph content by clicking, dragging, and zooming, viewing the specific writing information corresponding to different nodes and relationships. The system also incorporates feedback portals within the interface, such as text input boxes and option buttons, allowing users to instantly enter feedback on the writing suggestions, including their satisfaction with the suggestions, their degree of adoption, and suggested revisions. Upon receiving feedback, the system immediately passes it to the feedback processing module, which analyzes the feedback according to pre-set rules and extracts key information, such as negative user responses to certain suggestions or in-depth research on specific topics. Based on this feedback, the system adjusts the semantic graph model in real time, such as adding or removing nodes and modifying relationship weights. It also optimizes the writing suggestion generation strategy, such as adjusting the parameters of the recommendation algorithm and changing the priority of suggestion presentation. The adjusted models and strategies are immediately applied to subsequent writing support. At the same time, the system continuously monitors new feedback from users and continuously iterates and optimizes to form a closed-loop optimization cycle to ensure continuous improvement in the effectiveness of writing support.

[0054] By developing interactive visualization tools, users can browse and manipulate semantic graph content in an intuitive and convenient way, gaining an in-depth understanding of the knowledge sources and logical basis of writing suggestions, which greatly enhances users' understanding and trust in the writing support system and improves the user experience. It supports users to provide instant feedback on writing suggestions and to modify the graph model and generation strategy in real time based on the feedback, so that the system can accurately capture changes in user needs and respond quickly, thus achieving personalized and dynamic optimization of writing support. This closed-loop optimization mechanism is like equipping the writing support system with a self-evolution engine, enabling it to continuously learn and adapt to users' writing habits and needs, continuously improving the quality and practicality of writing suggestions, thereby providing users with better quality and more intimate writing assistance in long-term use, effectively promoting the development of intelligent writing support technology towards a higher level of intelligence and humanization.

[0055] In summary, the present invention achieves this by: through the real-time dynamic construction and updating of semantic graphs, making writing support closely aligned with user intentions and context changes, thus avoiding the problem of disconnected writing suggestions in traditional systems. The system uses deep learning algorithms to extract user preferences and optimizes context feature vectors in combination with network trends, which can accurately capture hot topics and incorporate them into writing suggestions. The combination of attention mechanism and writing rhythm parameters improves the ability to identify writing difficulties and makes the expansion of semantic graphs more accurate. The call to the preset concept association database and domain expert knowledge base ensures the rationality of node addition and semantic relationship update, and enhances the scientific nature and accuracy of writing suggestions. The application of multi-strategy path search algorithm and audience preference model realizes personalized adaptation and precise presentation of writing suggestions. The introduction of quantum computing and adaptive rapid stream neural network architecture improves the efficiency of model optimization and ensures the real-time nature of writing support. The combination of interactive visualization tools and user feedback mechanism forms a closed-loop optimization mechanism to continuously improve the writing support effect. The present invention improves the performance and user experience of the intelligent writing support system from multiple dimensions. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent writing support method based on semantic graph, characterized in that: The following steps are involved: When writing begins, the system analyzes the initial writing information and user preferences, determines the writing intention and context, generates an initial context feature vector, and builds a basic semantic graph model with node and relationship weights based on this. At the same time, the model is input into the real-time monitoring module for subsequent writing monitoring and dynamic adjustment of the semantic graph. The real-time monitoring module analyzes user input during the writing process, dynamically evaluates writing intentions and context changes, generates an intention-context state vector and passes it to the semantic graph dynamic processing module, triggering the dynamic expansion and updating of the semantic graph. At the same time, the updated semantic graph model is input into the writing suggestion generation module to generate personalized writing suggestions. After receiving the intention-context state vector, the semantic graph dynamic processing module adds relevant nodes according to the changes in writing intention, updates the semantic relationships and weights based on the context state, and uses the graph neural network algorithm to optimize the semantic graph model to maintain consistency and coherence, and then outputs the model to the writing suggestion generation module. The writing suggestion generation module uses the updated semantic graph model to find the knowledge path associated with the current writing node, determine the writing ideas and expansion direction, adapt the style and expression method based on the audience information, and finally present the generated writing suggestion content to the user in a visual way to assist writing.

2. The intelligent writing support method based on semantic graph according to claim 1, characterized in that: When analyzing initial writing information and user preferences, the system uses a deep learning algorithm to extract user writing history features, dynamically adjusts the initial context feature vector based on real-time network data, and integrates user preferences and real-time trend information through feature fusion technology to optimize the basic semantic graph model construction process and enhance the model's responsiveness to hot topics.

3. The intelligent writing support method based on semantic graph according to claim 1, characterized in that: When analyzing user input, the real-time monitoring module focuses on key semantic units of the text through an attention mechanism, introduces writing rhythm parameters, judges the degree of writing difficulty based on the user's input speed and pause time, dynamically adjusts the intention-context state vector generation strategy, and combines context semantic consistency verification to improve vector transmission accuracy and optimize the dynamic expansion trigger mechanism of the semantic graph.

4. The intelligent writing support method based on semantic graph according to claim 1, characterized in that: When adding related nodes, the semantic graph dynamic processing module calls the preset concept association database to quickly locate high-value concept nodes. When updating semantic relationships based on context status, it uses the domain expert knowledge base rules to verify the rationality of the update. Through the graph structure consistency verification algorithm, it ensures the coherence and accuracy of the semantic graph model, optimizing the basis for generating writing suggestions.

5. The intelligent writing support method based on semantic graph according to claim 1, characterized in that: When searching for knowledge paths, the writing suggestion generation module uses a multi-strategy path search algorithm to comprehensively evaluate path factors, screen the optimal knowledge path combination, and build an audience preference model to analyze the characteristics of different audience groups, thereby achieving personalized adaptation of writing suggestion styles and expressions and improving the accuracy of suggestions.

6. The intelligent writing support method based on semantic graph according to claim 1, characterized in that: When optimizing the semantic graph model, the semantic graph dynamic processing module introduces quantum computing parallel processing technology to accelerate large-scale data operations, and designs an adaptive graph neural network architecture to automatically adjust network parameters according to dynamic changes in the graph, thereby improving model optimization efficiency and ensuring real-time writing support.

7. The intelligent writing support method based on semantic graph according to claim 1, characterized in that: When presenting writing suggestions, the writing suggestion generation module develops an interactive visualization tool to allow users to directly browse the graph content and supports users to provide instant feedback on writing suggestions. The system corrects the graph model and suggestion generation strategy in real time based on the feedback, forming a closed-loop optimization mechanism to continuously improve the writing support effect.

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