Text reading cognitive analysis method and system

Abstract

The invention discloses a text reading cognitive analysis method and system, and belongs to the technical field of artificial intelligence education. According to the method, a macroscopic-mesoscopic-microscopic three-layer analysis architecture is constructed, so that a technical breakthrough from grammar surface analysis to cognitive deep analysis is realized. The method specifically comprises the steps of identifying a text type based on a static literary and schema model library; a three-layer architecture is adopted for structural analysis, a macroscopic layer extracts a chapter logic framework and generates a memory anchoring thinking body, a middle layer analyzes paragraph functions and writing techniques, and a microscopic layer quantitatively analyzes logic relations between sentences; and generating a logic chain visualization result and providing interactive training guidance. Compared with the prior art, the technical limitation that traditional text analysis can only process surface language features is broken through, and the technical problems of text logic structure deep analysis, cultural thinking difference recognition and personalized training generation are solved through an innovative three-layer analysis architecture and a memory anchoring thinking body technology.

Description

I. Technical Field

[0001] This invention belongs to the interdisciplinary field of artificial intelligence education technology and natural language processing, specifically involving a text reading cognitive analysis method and system, and in particular an intelligent text cognitive analysis scheme based on a macro-meso-micro three-layer analysis architecture that can realize the structured parsing of logical chains and the visualization of memory anchoring.

[0002] This invention specifically covers the following technical directions:

[0003] 1. Intelligent Text Analysis: Involving the application of deep learning and graph neural networks in text structure analysis.

[0004] 2. Cognitive computing: including technologies such as logical relationship quantification, thought pattern modeling, and cognitive load assessment.

[0005] 3. Educational Artificial Intelligence: Covering applications such as personalized training generation and adaptive learning path planning.

[0006] 4. Visualized Human-Computer Interaction: This includes technical fields such as logic chain visualization and interactive exploration interfaces.

[0007] The technical solution of this invention mainly addresses the technical deficiencies of traditional text analysis systems in terms of logical depth analysis, thinking pattern recognition, and cognitive training generation, and belongs to the innovative application of artificial intelligence technology in the field of educational cognition. II. Background Technology

[0008] Existing text analysis technologies face the following technical bottlenecks in educational applications:

[0009] 1. Technical shortcomings due to insufficient analytical depth

[0010] Current mainstream text analysis tools (such as Stanford Parser and SPACY) are mainly based on dependency parsing and constituent parsing techniques, and their technical limitations are reflected in:

[0011] - It can only handle grammatical structures at the single-sentence level and lacks the ability to identify logical relationships across sentences.

[0012] - The inability to establish a macro-logical framework for the text makes it difficult for learners to grasp the overall argumentative structure. - The analysis results remain at the surface level of linguistic features, failing to reveal the deeper thought processes and organizational patterns of the text.

[0013] 2. Gaps in the analysis of cultural differences in thinking

[0014] In cross-cultural language learning scenarios, existing technologies have significant shortcomings:

[0015] - Lack of quantitative analysis models for the differences between Chinese and Western thinking patterns (e.g., Chinese "spiral" logic vs. British "linear" logic) - Inability to identify cultural thinking characteristics reflected in texts, making it difficult for learners to understand the expression logic in different cultural contexts - Existing systems (e.g., CN113657125A) do not consider the influence of cultural factors in their structure identification methods, resulting in limited adaptability. 3. The disconnect between training and analysis.

[0016] The text analysis systems of mainstream learning platforms on the market (such as Yuanfudao and Zuoyebang) have training defects:

[0017] - The generated training content is disconnected from the deep logical structure of the text, failing to effectively improve thinking skills. - The training program remains at the superficial level of Bloom's Taxonomy (memory, comprehension), lacking higher-order thinking training such as analysis, evaluation, and creation.

[0018] - The inability to dynamically adjust training difficulty based on text analysis results violates the "zone of proximal development" educational theory.

[0019] 4. Inherent limitations of the technical architecture

[0020] The existing system's technical implementation methods have fundamental constraints:

[0021] - Rule-based methods have limited coverage and struggle to handle complex linguistic phenomena.

[0022] - Statistical learning methods (such as LDA models) have poor interpretability and cannot provide a clear logical analysis path. - The "black box" nature of deep learning models (BERT, GPT, etc.) makes the analysis process untraceable and uninterpretable. 5. Insufficient visualization.

[0023] Existing visualization solutions (such as tree diagrams and mind maps) have limitations in their expressive capabilities:

[0024] - The dynamic construction process of the logical chain cannot be clearly shown.

[0025] - Difficult to express the relationships between multi-level text structures (macro-meso-micro).

[0026] - The lack of interactive logic exploration features limits the deep learning experience.

[0027] To address the aforementioned technical deficiencies, this invention proposes a text reading cognitive analysis scheme based on a three-layer analysis architecture, achieving a breakthrough in in-depth analysis from the grammatical level to the cognitive level.

[0028] "Technical issues": "More specific and clear"

[0029] "Logical hierarchy": "More clear and rigorous"

[0030] "Professional Depth": "More Technology-Oriented"

[0031] "Critical Targeting": "More Precise and Powerful" III. Summary of the Invention

[0032] (a) Technical problems to be solved

[0033] This invention aims to solve the following technical problems existing in the application of text analysis technology in educational scenarios:

[0034] 1. The problem of insufficient depth in parsing the logical structure of the text.

[0035] Existing text analysis techniques mostly focus on analyzing surface-level linguistic features such as vocabulary and grammar, lacking the ability to analyze the deep logical structure of texts (such as argumentation relationships, reasoning chains, and thought processes), thus failing to meet the needs of in-depth reading cognitive training.

[0036] 2. The problem of missing analysis of cultural differences in thinking

[0037] Traditional systems fail to adequately consider differences in thinking patterns across different language and cultural backgrounds, and lack the ability to identify and analyze culturally specific expressions and logical organization habits, resulting in limited effectiveness in cross-cultural text comprehension.

[0038] 3. The problem of training-generated knowledge being disconnected from cognitive principles.

[0039] In existing technologies, text analysis and training guidance are independent of each other, and the generation of training content fails to fully integrate the user's cognitive level, memory patterns, and learning paths, resulting in poor training effects.

[0040] 4. Issues related to the scalability and adaptability limitations of the technical architecture.

[0041] Traditional system architectures struggle to support multi-layered and multi-granular text analysis needs, and cannot flexibly adapt to different text types and application scenarios, thus limiting the practical application scope of the technology.

[0042] 5. The problem of mismatch between visual representation and cognitive load.

[0043] Existing visualization solutions fail to effectively balance information density and cognitive load, and cannot present complex logical relationships intuitively in a way that conforms to human cognitive habits.

[0044] (II) Overall Technical Solution

[0045] This invention addresses three major technical bottlenecks in existing text analysis techniques: (1) insufficient depth of logical structure parsing;

[0046] (2) The analysis of cultural thinking differences is lacking, and (3) the training generation is disconnected from cognitive laws. Therefore, a text reading cognitive analysis method and system based on logical chain structure is proposed. The core technical breakthrough of the method lies in the construction of a three-level analysis architecture of macro-meso-micro. By introducing a memory model and a logical chain visualization mechanism, a technical leap from traditional grammatical analysis to cognitive analysis is achieved.

[0047] The technical solution of this invention is achieved through the following core modules:

[0048] 1. Construction of a multi-dimensional static text style model library

[0049] Establish a comprehensive model library covering seven major writing styles (argumentative essays, expository essays, practical documents, academic papers, business reports, administrative documents, and press releases), with each style model including:

[0050] -Structural Feature Matrix: Quantitatively represents the organizational patterns and framework characteristics of a text style.

[0051] - Mindset Vector: Embedded with cultural mindset difference parameters (such as spiral / linear logical tendency values).

[0052] -Language style fingerprint: Defines the unique expression methods and rhetorical preferences of a writing style.

[0053] - Cognitive Difficulty Level: A comprehension threshold indicator set based on reading psychology research.

[0054] 2. Three-tiered progressive analysis architecture

[0055] An innovative hierarchical analysis system based on cognitive science is proposed:

[0056] Macro level (logical structure of the text)

[0057] - Employs a global logical relationship extraction algorithm based on graph neural networks.

[0058] - Construct a three-dimensional map of the text's argumentative framework and thought process.

[0059] - Enable dynamic tracking and visualization of cross-paragraph logic flow

[0060] Mesoscopic level (segment functional network)

[0061] - Develop a paragraph role classifier (argument paragraph, supporting argument paragraph, transition paragraph, etc.)

[0062] -Establish a quantitative model for the logical connection strength between paragraphs

[0063] - Generate a function-oriented paragraph relationship topology map

[0064] Micro-level (sentence logic unit)

[0065] - Design a multi-granularity logical connector recognition engine

[0066] -Analysis of the relationship between rhetorical devices and logical functions

[0067] - Construct a sentence-level logical density evaluation index system

[0068] 3. Memory Anchoring Mindset Generation System

[0069] Based on the principles of cognitive psychology, a logical memory model with physical perception characteristics is created:

[0070] Logical framework extraction module

[0071] -Key information extraction algorithm enhanced with attention mechanism

[0072] -Establish an importance weight calculation model for logical nodes

[0073] - Generate a hierarchical logical tree structure

[0074] Key symbol mapping engine

[0075] -Developing a concept-a two-way mapping dictionary of symbols

[0076] -Concrete symbolic representation of abstract logical relationships

[0077] - Constructing a symbol selection strategy based on cultural background

[0078] Visual cue generator

[0079] - Applying principles of color psychology to design visual coding schemes

[0080] - Develop cognitive load optimization algorithms for spatial layout

[0081] - Generate personalized memory-anchoring visual cues

[0082] (III) Technical Implementation Details at Each Level

[0083] (1) Specific implementation of macro-level analysis

[0084] The macroscopic layer analysis employs a structure analysis algorithm based on a multimodal graphical neural network, and the specific technical implementation is as follows:

[0085] 1. Intelligent Text Structure Recognition System

[0086] - Stylistic awareness encoding mechanism: A hybrid architecture is adopted, which guides the allocation of attention weights through stylistic feature vectors to achieve type-adaptive structural parsing.

[0087] - Dynamic template matching algorithm: Develop an adaptive threshold adjustment mechanism based on similarity calculation, comprehensively considering text complexity and structural variability.

[0088] - Multi-scale feature fusion: Integrates multi-level language features and improves the parsing accuracy of non-standard text structures through a dynamic weighting mechanism.

[0089] 2. Memory Anchoring Mindset Generation Engine

[0090] Based on cognitive science theory, a three-tiered memory anchoring system is constructed:

[0091] Logical framework extraction module

[0092] - An improved rhetorical structure parser is employed, incorporating quantitative cultural thinking characteristic parameters.

[0093] - Construct a rhetorical relationship analysis network based on probabilistic reasoning to resolve ambiguity issues in traditional analysis. - Implement quantitative scoring of logical relationships to provide data support for visualization.

[0094] Key symbol mapping system

[0095] - Establish a concept importance assessment model based on multi-dimensional feature fusion

[0096] - Develop a knowledge graph-based algorithm for concept association mining to automatically discover hidden logical relationships.

[0097] - Design a cross-cultural symbol mapping rule base to adapt to the cognitive habits of different cultural backgrounds.

[0098] Visual cue generation pipeline

[0099] - Applying Gestalt psychology principles to design visual coding rules

[0100] - Develop a visual complexity control algorithm based on cognitive load theory

[0101] - Enables personalized visual solution generation, supporting adaptation to learners' cognitive preferences.

[0102] (2) Technological innovation in meso-level analysis

[0103] Mesoscopic analysis overcomes the limitations of traditional technologies through a multi-task deep learning framework:

[0104] 1. Paragraph-based multi-tag classification system

[0105] Constructing a multi-granularity classification architecture based on hierarchical Transformer:

[0106] Hierarchical attention mechanism

[0107] -Local attention: capturing linguistic features within a paragraph

[0108] -Global attention: Analyzing the logical connections between paragraphs

[0109] - Cross-document attention: Learning from the structural patterns of similar texts

[0110] Multi-task learning optimization

[0111] -Main Task: A Detailed Classification System for Paragraph Functions

[0112] -Auxiliary tasks: writing style recognition, logical role labeling, sentiment analysis- Improve model generalization ability through learning the correlation between tasks.

[0113] Increased combat training

[0114] - Introducing a gradient inversion layer enhances the model's robustness to stylistic variations. - Employing domain adversarial training eliminates distribution bias in the training data.

[0115] - Solve the class imbalance problem through data augmentation techniques

[0116] 2. In-depth analysis engine for writing techniques

[0117] Establish a two-way mapping model between writing techniques and logical functions:

[0118] Integrated analysis of rules and statistics

[0119] - Rule layer: Formal definition and pattern recognition of various writing techniques - Statistical layer: Context-aware classifier based on deep learning

[0120] - Reasoning Layer: Knowledge Reasoning Network with Logical Function Mapping

[0121] Logical Function Quantitative Assessment

[0122] -Develop quantifiable metrics for writing techniques effectiveness

[0123] - Establish a correlation model between the frequency of technique usage and text quality.

[0124] - Provide actionable improvement suggestions based on technique analysis

[0125] (3) Technological breakthroughs in micro-layer analysis

[0126] Micro-level analysis achieves the following technological breakthroughs through a multimodal fusion analysis framework:

[0127] 1. Logical Relationship Quantitative Analysis System

[0128] Building a logical relationship analysis engine based on deep semantic understanding:

[0129] Logical strength calculation of multi-feature fusion

[0130] - A logic strength calculation model based on multi-dimensional feature fusion, with parameters dynamically adjusted according to stylistic features - comprehensively considering conjunction weights, semantic relevance, contextual coherence, and rhetorical enhancement factors.

[0131] Building a semantic library of connectors

[0132] - Establish a multi-level classification knowledge base of logical connectors

[0133] - Assign quantified logical strength weights to conjunctions.

[0134] - Develop a context-aware algorithm for dynamically adjusting the weights of connectors.

[0135] Semantic relevance analysis

[0136] - Calculate semantic relevance using sentence vector representation techniques

[0137] - Introduce attention mechanisms to capture semantic associations of key information

[0138] - Implement fine-grained semantic similarity evaluation

[0139] Contextual coherence detection

[0140] - Contextual dependencies in logical relationships based on sequence modeling

[0141] - Construct a logical coherence scoring model to assess the integrity of the logical chain.

[0142] - Implement continuity detection across paragraph logic flows

[0143] 2. Rhetorical Device Functional Recognition Engine

[0144] Establish a deep mapping system between rhetorical devices and logical functions:

[0145] Rhetorical Function Classification System

[0146] - Construct a multi-level classification system of rhetorical functions, establishing a mapping relationship between rhetorical devices and logical functions - covering major functional categories such as concept construction, argument progression, emotion regulation, and structural organization.

[0147] Deep learning recognition network

[0148] - Employs a multi-task learning framework to simultaneously perform rhetoric recognition and functional classification.

[0149] - Construct a dual-encoder architecture to analyze rhetorical forms and logical functions separately.

[0150] - Achieving aligned learning of form and function through attention mechanisms

[0151] Functional effectiveness quantitative evaluation

[0152] -Develop a quantitative index system for the effectiveness of rhetoric.

[0153] -Establish a multi-dimensional evaluation system for the quality of rhetorical use.

[0154] - Provide actionable improvement suggestions based on quantitative analysis

[0155] Cross-cultural rhetoric adaptation

[0156] -Analyze the differences in rhetorical functions between China and the West

[0157] - Construct culturally sensitive rhetorical function mapping rules

[0158] -Improve personalized features based on user cultural background

[0159] (iv) System Architecture Design

[0160] This invention adopts a cloud-native microservice architecture and constructs the following core module system:

[0161] 1. Intelligent text input and preprocessing module

[0162] - Multi-format adaptive input engine: Supports intelligent parsing of various common document formats, employing a deep learning-based layout recovery algorithm to achieve high-accuracy text extraction.

[0163] - Text Quality Assessment Subsystem: Integrates multi-dimensional quality assessment indicators, generates quality reports and improvement suggestions in real time. - Encoding Recognition and Standardization Pipeline: Automatically detects character encoding, converts it to a standardized format, and has efficient batch processing capabilities.

[0164] 2. Multi-level distributed analysis engine

[0165] - Microservice governance architecture: Each analytics layer is deployed independently, with service discovery and load balancing achieved through a service mesh. - Unified data exchange protocol: A highly efficient serialization protocol is designed to significantly improve data transmission efficiency. - Pipeline parallel processing: Parallel execution of macro, meso, and micro-level analyses is achieved, greatly reducing overall processing time. 3. Intelligent visualization generation module

[0166] - WebGL 3D rendering engine: Supports 3D visualization of logical chains and has the capacity to handle large-scale nodes. - Dual-mode intelligent switching system:

[0167] - Analysis Mode: Displays the complete logical topology, supporting node drill-down and relationship tracing.

[0168] - Training Mode: Highlights weak areas and provides a focused training view.

[0169] - Real-time interactive analysis: Supports rich interactive operations and achieves low-latency real-time response.

[0170] 4. Adaptive Training Guidance Generation Module

[0171] - Personalized training solution engine: Generates customized solutions based on learner profiles.

[0172] - Dynamic Difficulty Adjustment Algorithm: Monitors training performance in real time and dynamically adjusts difficulty parameters based on reinforcement learning. - Intelligent Progress Management System: Optimizes training pace and content reproduction strategies through sequence prediction models.

[0173] (V) Technological Advantages and Innovations

[0174] 1. Innovation in theoretical framework

[0175] - Proposes for the first time a logical chain-structured cognitive theory, establishing a complete analytical system from the surface level of language to the deep level of thought. - Innovates the concept of anchoring thought entities in memory, realizing the concrete representation and persistent memory of abstract logic.

[0176] 2. Technological Architecture Innovation

[0177] - Three-tier collaborative analysis architecture: Breaks through the limitations of traditional single-layer analysis, achieving full-stack text understanding. - Cross-modal fusion learning: Integrates textual, logical, and visual multimodal information to improve analysis accuracy. - Elastic microservice design: Supports horizontal scaling and has elastic scaling capabilities for high-concurrency requests.

[0178] 3. Algorithm Model Innovation

[0179] -Hierarchical Transformer: Achieving high accuracy in paragraph function recognition tasks

[0180] - Multi-task adversarial learning: Enhances model generalization ability and improves cross-domain adaptability.

[0181] - Quantitative Calculation of Logical Strength: A pioneering numerical evaluation system for logical relationships.

[0182] 4. Application Model Innovation

[0183] -Analysis-Training Closed-Loop System: Realizing the Complete Link from Cognitive Diagnosis to Capability Enhancement

[0184] - Cross-cultural adaptive mechanism: Automatically identifies and adapts thinking patterns from different cultural backgrounds.

[0185] - Real-time feedback optimization loop: Continuously improve analysis quality based on user behavior data.

[0186] (vi) Technical Effect Description

[0187] Compared with existing technologies, this invention, through its innovative three-layer analysis architecture and memory-anchored thought body technology, produces the following beneficial effects:

[0188] 1. Expanded the dimensions of text analysis.

[0189] By constructing a three-tiered collaborative analysis framework of macro-meso-micro, a technical path from surface-level grammatical analysis to deep-level cognitive analysis is built, providing technical means for in-depth analysis of the internal logical structure of text.

[0190] 2. Improved the adaptability of training programs.

[0191] Based on the results of logical chain structured analysis and memory anchoring technology, personalized training programs that match the user's cognitive characteristics can be generated, enhancing the relevance of training guidance.

[0192] 3. It expands the scope of application of the technical solution.

[0193] It supports various document types, including academic papers, business reports, and administrative documents, and is suitable for a wide range of applications, from basic education to professional writing.

[0194] 4. Improved the cognitive efficiency of knowledge representation.

[0195] By using memory anchoring techniques to transform abstract logical relationships into symbolic visual representations, it helps to improve the understanding and memorization of knowledge.

[0196] 5. Optimized the utilization efficiency of system resources.

[0197] By employing a hierarchical optimization strategy, it becomes possible to smoothly process complex text structures on ordinary computing devices.

[0198] (vii) Feasibility analysis

[0199] 1. Technology maturity

[0200] -Developed based on a mature artificial intelligence framework

[0201] -The core algorithm has sufficient theoretical support.

[0202] Modular design effectively reduces system integration complexity.

[0203] 2. Deployment flexibility

[0204] - Supports multiple deployment environments to meet different application needs

[0205] - Employing containerization technology simplifies deployment and maintenance processes.

[0206] - Provides standardized API interfaces for easy system integration.

[0207] 3. Scalability Guarantee

[0208] - Adopts a plug-in architecture, supporting flexible expansion of functional modules.

[0209] - Reserves an algorithm upgrade interface to support continuous model optimization

[0210] - Design standardized data specifications to facilitate ecosystem development

[0211] 4. Industrialization Foundation

[0212] - Possesses a complete technical implementation plan

[0213] - Possesses large-scale data processing capabilities

[0214] -Establish a comprehensive intellectual property protection system IV. Description of the attached drawings

[0215] Figure 1 This is a block diagram of the overall architecture of the system of the present invention.

[0216] The diagram showcases the core architecture of the text reading and cognitive analysis system, comprising five major components: a text input interface, a text style recognition engine, a logic chain analysis engine, a visualization generation module, and a training guidance module. Arrows in the diagram indicate the data flow relationships between these modules.

[0217] Figure 2 This is a schematic diagram of a three-level analysis flowchart.

[0218] It demonstrates a three-tiered analysis process—macro, meso, and micro—including a complete workflow of text input, macro analysis (text structure), meso analysis (paragraph function), micro analysis (sentence relationships), and result output. The progressive relationships between each level are clearly defined by connecting lines.

[0219] Figure 3 It is a visual interface diagram.

[0220] The diagram shows the layout of the system output interface, including the original text display area, the logic chain diagram display area, and the memory anchoring thought model display area. The layout of the mode switching controls is also shown.

[0221] Figure 4 This is a diagram of training guidance generation.

[0222] The training scheme generation process is demonstrated, including the basic steps of analysis result input, weak link identification, user level assessment, training content generation, and scheme output. V. Detailed Implementation Methods

[0223] Example 1: Taking the in-depth logical analysis of academic papers as an example, the system identifies its "introduction-method-result-discussion" structure, generates a logical framework diagram at the macro level, marks writing techniques such as "example-argumentation" and "data comparison" at the meso level, analyzes the causal relationship between sentences at the micro level, and generates a memory anchoring mindset to help users understand and remember.

[0224] 1.1 Input and Preprocessing

[0225] The system receives academic papers uploaded by users and first performs text extraction and cleaning. Specifically, this includes extracting the original text from PDF documents, removing formatting tags such as "abstract" and "keywords," standardizing citation formats, and performing paragraph segmentation and structural marking. The system uses a rule-based method to identify the paper's chapter structure, including sections such as introduction, methods, results, and discussion, laying the foundation for subsequent analysis.

[0226] 1.2 Implementation of Macro-level Analysis

[0227] At the macro level, the system first identifies the overall structural framework of the paper. A pre-trained deep learning model categorizes each paragraph into chapters, determining the position of each paragraph within the paper's logical structure. It then extracts the logical relationships at the chapter level, including causal, comparative, and progressive relationships. Finally, it generates a memory anchoring framework, which includes the paper's core logical framework, symbolic mappings between key concepts, and visual cues to aid memory.

[0228] The visualization uses an interactive logic flow diagram format, with different chapters distinguished by color coding and logical relationships represented by arrows of different line types, providing users with an intuitive global logical view.

[0229] 1.3 Details of Mesoscopic Analysis

[0230] At the meso-level, the system focuses on analyzing paragraph function and writing techniques. For key paragraphs such as "Research Methods," the system identifies the writing techniques used, including definition, exemplification, comparison, and classification. Accuracy is ensured by combining rule matching and deep learning model validation.

[0231] Paragraph function analysis employs a multi-task learning model, which considers both the linguistic features of the paragraph itself and its contextual information within the entire text, to accurately determine the specific function that the paragraph plays in the logical chain of the entire text.

[0232] 1.4 Implementation of Microscopic Layer Analysis

[0233] At the micro level, the system performs sentence-level logical relationship analysis. Semantic role labeling technology is used to identify the semantic functions of each component in a sentence, extract logical connectors, and analyze the use of rhetorical devices. Based on this, a logical network between sentences is constructed, establishing a complete micro-logical chain.

[0234] Example 2: Taking the cross-cultural logic analysis of a business report as an example, the system analyzes its "general-specific-general" structure, marks writing techniques such as "classification description" and "cited data", and visualizes the argumentation path through the logic chain.

[0235] 2.1 Implementation of Multilingual Support

[0236] The system is specifically optimized for business reports that combine Chinese and English. First, a language detection algorithm identifies different language segments in the text. Then, the corresponding processing pipelines are invoked for analysis. Chinese processing focuses on understanding idioms, proverbs, and culturally specific expressions, while English processing emphasizes sentence structure and logical connectors. Finally, the analysis results from each language segment are organically integrated.

[0237] 2.2 Analysis of Cross-Cultural Thinking Patterns

[0238] The system extracts thought pattern features from texts, including argumentation style, evidence type, and conclusion derivation logic. Through a cultural thought pattern classifier, it identifies the characteristics of Chinese and Western thinking reflected in the text. It calculates cultural difference levels to provide users with targeted cross-cultural communication suggestions, helping them understand the logical expression characteristics of different thought patterns.

[0239] 2.3 Identification of Business-Specific Writing Techniques

[0240] The system has a built-in rule library specifically for business writing, which includes recognition patterns for specific sections of business reports such as executive summaries, market analysis, and recommended actions. Each pattern is associated with a corresponding logical function, such as "providing market data support" or "proposing action recommendations," ensuring that the analysis results align with the needs of business practice.

[0241] Example 3: Generation of Personalized Training Guidance

[0242] 3.1 Generation of Personalized Training Plans

[0243] Based on text analysis results, the system identifies users' weaknesses in logical thinking and generates personalized training plans based on their current level. The difficulty of the training content is dynamically adjusted, including various forms such as logical fill-in-the-blank, paragraph restructuring, and specific writing techniques exercises, ensuring that the training content is both challenging and in line with the user's actual level.

[0244] 3.2 Real-time feedback mechanism

[0245] After users complete the training task, the system evaluates them from two dimensions: logical consistency and writing style application. Logical consistency checks ensure that the user's answers conform to the internal logic of the text, while writing style evaluation focuses on the appropriateness of the expression. The system generates specific improvement suggestions and recommends subsequent learning paths, forming a complete learning loop.

[0246] Example 4: Implementation of System Performance Optimization

[0247] 4.1 Distributed Analysis Architecture

[0248] To handle large-scale text data, the system adopts a distributed architecture design. Macro-level analysis, meso-level analysis, and micro-level analysis tasks are distributed to different computing nodes for parallel execution. The document is first divided into text blocks of appropriate size, and each analysis node independently processes its assigned text block. Finally, a unified analysis report is generated through a dedicated results integration module.

[0249] 4.2 Caching and Performance Optimization

[0250] The system establishes a multi-level caching mechanism, storing analysis results based on text feature hash values. Cache validity checks comprehensively consider text features and the analysis model version, ensuring accuracy of analysis results while improving performance. A least recently used strategy is employed to manage cache space, optimizing system resource utilization.

[0251] Example 5: Adaptation to Special Scenarios

[0252] 5.1 Customization for Educational Institutions

[0253] To meet the educational needs of different educational stages, the system offers configurable depth of analysis and visualization complexity. The basic education stage focuses on identifying fundamental logical structures and providing simple diagrams, while the higher education stage offers in-depth logical analysis and interactive visualization tools, ensuring the system's applicability in various educational scenarios.

[0254] 5.2 Customized Corporate Training

[0255] The system adjusts its analytical focus based on the characteristics of different industries. The financial industry emphasizes data analysis and risk assessment logic, while the marketing industry focuses on persuasion techniques and customer analysis logic. By loading industry-specific rules and benchmark data, it provides industry-specific analytical results and training suggestions.

[0256] VI. Technical Details

[0257] The following technical details apply to each embodiment: 1. Construction of memory anchoring mind body

[0258] In various embodiments of the present invention, the construction of the memory anchoring mind body includes three core components: analyzing the rhetorical relationships of a text based on rhetorical structure theory to extract a logical framework; identifying the core concepts in the text and their relationships; and creating a corresponding visual symbol system based on the type of logical relationship.

[0259] 2. Logical Relationship Strength Assessment

[0260] In a preferred embodiment of the present invention, the strength of the logical relationship is calculated through a combination of multiple features, including but not limited to: the strength weight of logical connectors, semantic similarity assessment, and contextual coherence analysis. These features are weighted and combined to form the final relationship strength score.

[0261] 3. Visual rendering optimization

[0262] In various embodiments, the large-scale logic graph rendering employs a hierarchical optimization strategy to ensure that complex logic structures can be smoothly displayed even on ordinary computing devices.

Claims

1. A text reading cognitive analysis method, characterized in that, Includes the following steps: -Receive input text; - Identify text types based on a static text style model library; - A three-tiered analysis framework of macro-meso-micro is used to perform structured parsing of the text; - Visualization results of generating logical chains and memory-anchored thought entities; - Provide interactive training guidance based on the analysis results.

2. The method according to claim 1, characterized in that, The macroscopic analysis includes: -Analyze the overall logical structure of the text; - Generate memory anchoring thought structures at the chapter level.

3. The method according to claim 1, characterized in that, The mesoscopic analysis includes: - Identify paragraph function types; -Analyze the logical connections between paragraphs.

4. The method according to claim 1, characterized in that, The micro-layer analysis includes: - Quantitative analysis of the strength of logical relationships between sentences; - Identify rhetorical devices and their logical functions.

5. The method according to claim 1, characterized in that, The static text model library contains structural feature matrices and thought pattern feature vectors for various text styles.

6. The method according to claim 1, characterized in that, The visualization results of the logic chain support dual-mode display of analysis mode and training mode.

7. The method according to claim 1, characterized in that, The interactive training guidance includes the generation of personalized training schemes based on the identification of logical weaknesses.

8. The method according to claim 1, characterized in that, The construction of the memory-anchored mindset includes: -Analyze the rhetorical relationships within a text based on rhetorical structure theory, and extract the logical framework; - Identify the core concepts in the text and the relationships between them; - Create a corresponding visual symbol system based on the logical relationship type.

9. The method according to claim 1, characterized in that, The strength of the logical relationship is evaluated by weighting the following features: - The strength weight of logical connectors; - Semantic similarity assessment; - Contextual coherence analysis.

10. The method according to claim 1, characterized in that, The visualization rendering employs a hierarchical optimization strategy to ensure smooth display of complex logical structures on ordinary computing devices.

11. A text reading cognitive analysis system implementing the method as described in any one of claims 1-10, characterized in that, include: - Text input and preprocessing module, used to receive, clean, and structure input text; - A text style recognition engine, used to call a static text style model library to recognize text types; - A multi-level analysis engine for performing macro-meta-micro three-level analysis; - Visualization generation module, used to generate visualization results of logical chains and memory-anchored thought entities; - The training guidance module provides interactive training guidance.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-10.

13. A text reading cognitive analysis device, characterized in that, include: - A memory for storing the computer program as described in claim 12; - A processor for executing the computer program to implement the method as described in any one of claims 1-10.

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