A method for processing network multi-source financial text big data

Abstract

The present invention belongs to the technical field of financial data processing and discloses a network multi-source financial text big data processing method, comprising collecting multi-source financial text data, performing semantic-level unstructured analysis, separating language critical points and triggering semantic breakpoints in semantic changes, constructing a financial fluctuation language trigger factor matrix, and capturing key expression fragments; receiving the trigger factor matrix, adopting a language expression motivation modeling mechanism, performing structure extraction and configuration mapping on the key expression fragments, constructing a configuration transformation graph with language expression motivation as the axis, and outputting configuration equivalent semantic clusters; performing topological structure analysis based on the configuration equivalent semantic clusters, identifying semantic fission points and mutation connections, determining semantic faults, and performing concept reconstruction on both sides of the fault to generate a concept semantic tree; achieving deep mining and structural modeling of semantic information of financial texts, and having important application value.

Description

Technical Field

[0001] The present invention relates to the technical field of financial data processing, and more specifically, to a method for processing network multi-source financial text big data. Background Art

[0002] Patent CN118467724A discloses a summary generation method and system based on a large financial model. The large financial model includes a key sentence extraction model and a summary generation model. The method includes: obtaining financial source data, preprocessing the financial source data to obtain financial text data; using the financial text data as input to the key sentence extraction model, extracting key sentences from the financial text data to obtain a number of key sentence vectors; using the several key sentence vectors as input to the summary generation model, compressing the several key sentence vectors to obtain a text summary; wherein the summary generation model is a sequence model using a convolutional neural network as a basic unit. This invention can quickly analyze and extract key sentences from different documents and reduce the impact of noise information. The summary generation model of this invention, which uses a convolutional neural network as a basic unit, not only enables parallel training of the model, but also improves the training efficiency of the summary generation model.

[0003] Existing methods for processing multi-source financial text big data on the Internet have the following drawbacks:

[0004] In the existing technology, most of the text clustering methods used are based on keywords or shallow grammatical rules, which are difficult to accurately capture financial text content with the same expression intention but large differences in form, resulting in limited semantic consistency recognition capabilities; traditional methods lack modeling of expression motivations and cannot establish equivalence relationships between text fragments with diverse structures, causing the same events or semantics to be fragmented during the analysis process, making it difficult to form a complete semantic view; there is a lack of expression normalization mechanism based on the fusion of structural configuration and expression motivation, resulting in discontinuous path logic and unstable deduction pathways in the process of financial semantic evolution modeling.

[0005] Existing financial semantic modeling technologies mostly use one-way extraction and graph construction, which are unable to systematically evaluate and provide feedback on the match between the constructed semantic structure and the original financial text. This leads to problems such as logical breaks and structural drift in the semantic graph, affecting the accuracy and completeness of the final semantic modeling. In multi-threaded semantic evolution scenarios, the predicted structure often deviates from the actual semantic evolution logic. Existing technologies lack precise definitions and measurement methods for semantic prediction errors and structural consistency errors, and are unable to provide effective indicator support for subsequent parameter optimization, resulting in the inability to effectively evolve the system's semantic understanding capabilities. Traditional configuration mapping functions and semantic fault determination rely on static threshold settings, which are difficult to maintain stable results under different corpora or contexts.

[0006] In view of this, the present invention proposes a network multi-source financial text big data processing method to solve the above problems. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned objectives, the present invention provides the following technical solution: a method for processing network multi-source financial text big data, comprising:

[0008] S1. Collect multi-source financial text data, conduct semantic-level unstructured analysis, identify critical language points and triggering semantic breakpoints in semantic changes, construct a language trigger factor matrix for financial fluctuations, and capture key expression fragments;

[0009] S2. Receive the trigger factor matrix, use the language expression motivation modeling mechanism to extract the structure and perform configuration mapping on the key expression fragments, construct a configuration transformation diagram centered on the language expression motivation, and output configuration equivalent semantic clusters;

[0010] S3. Based on the configuration-equivalent semantic clusters, perform topological structure analysis, identify semantic fission points and mutation connections, determine semantic faults, and perform concept reconstruction on both sides of the fault to generate a conceptual semantic tree;

[0011] S4. Based on the key event nodes in the concept semantic tree, a semantic evolution path is constructed to simulate the multi-directional propagation and transformation process of financial events, and a semantic multi-threaded evolution graph is generated by event flow reorganization.

[0012] S5. Reversely map the deduction path of the semantic multi-threaded evolution graph, calculate the actual deviation of the semantic evolution path and the structural consistency polarization value, adjust the semantic fault judgment and configuration mapping, and realize continuous optimization and iteration between semantic recognition logic and text processing.

[0013] Preferably, the method for performing semantic-level unstructured analysis includes:

[0014] Collect multi-source financial text data, including financial news data, policy announcement data, social media content data, corporate financial report text data, and analyst research reports; standardize the format of multi-source financial text data, remove redundant symbols, web page tags, and invalid content, and perform semantic-level unstructured analysis on the formatted financial text data;

[0015] Semantic-level unstructured analysis includes segmenting financial text data into semantic units, identifying financial entities, timing trigger words, sentiment words, and event expression structures; based on semantic unit segmentation, semantic annotation is performed through named entity recognition, part-of-speech tagging, and sentiment analysis, and a pre-trained language model is used to generate contextual embedding vectors.

[0016] Preferably, the method for capturing key expression fragments comprises:

[0017] Dynamic semantic difference analysis is performed on context embedding vectors to monitor the semantic evolution trajectory in the temporal structure and identify semantic segments in the context embedding vector that undergo jumps during the semantic expression process. A semantic change rate indicator is introduced to calculate the rate of change of the semantic vector difference between adjacent sentences or phrases to calibrate the semantic jump position during the semantic change process and identify it as a language critical point.

[0018] Analyze the expression structure before and after the language critical point, identify the expression paragraphs with mutations in syntactic structure and entity relationship, and define them as triggering semantic breakpoints; construct the financial volatility language trigger factor matrix based on the language critical point and triggering semantic breakpoints; and perform expression importance weight sorting to screen out key expression fragments whose expression importance weight is greater than the preset expression importance weight threshold.

[0019] Preferably, the method for performing structure extraction and configuration mapping comprises:

[0020] The method receives a financial volatility language trigger factor matrix, takes key expression segments contained in the financial volatility language trigger factor matrix as input, extracts the language context, grammatical structure, and event element information corresponding to the segments; and constructs an expression motivation map for each key expression segment based on the language expression motivation modeling mechanism.

[0021] The various expression motivations in the expression motivation map are classified and labeled, and grammatical structure extraction is performed. The structural configuration of key expression fragments is extracted by combining dependency syntax analysis, semantic role labeling and event trigger word recognition. Based on the structural configuration, a language configuration mapping table is constructed, and key expression fragments with similar expression motivations are structurally mapped and classified into a unified configuration category.

[0022] Preferably, the method of outputting configuration-equivalent semantic clusters includes:

[0023] For each key expression segment, a grammatical configuration mapping function is used to map it into a grammatical configuration vector. The grammatical configuration vector comprehensively represents the syntactic structure features, semantic role, and expression motivation label of the key expression segment. Taking a specific central expression segment as a benchmark, the semantic configuration distance between the key expression segment and the specific central expression segment in the configuration vector space is calculated using cosine similarity.

[0024] The semantic configuration distance is used to measure the similarity between two key expression fragments in terms of structure and semantic expression motivation. A configuration equivalence tolerance threshold is preset, and all key expression fragments that meet the configuration equivalence semantic cluster judgment relationship are classified into the same configuration equivalence semantic cluster. With each configuration equivalence semantic cluster as a node, a configuration transformation graph with language expression motivation as the axis is constructed.

[0025] Preferably, the method for determining semantic faults includes:

[0026] The configuration-equivalent semantic clusters are regarded as graph nodes, and a semantic structure graph is constructed. The edges in the semantic structure graph represent the semantic connection relationship between different configuration-equivalent semantic clusters. A topological structure analysis is performed on the constructed semantic structure graph. Nodes with an increased number of outgoing edges and a semantic pointing distribution difference greater than a preset semantic pointing distribution difference threshold are identified as semantic fission points. Connecting edges with a configuration vector distance greater than a preset configuration vector distance threshold are identified as mutation connections. The information of semantic fission points and mutation connections is integrated to determine the semantic fault area.

[0027] Preferably, the method for generating a concept semantic tree includes:

[0028] The core expression segments in the configurationally equivalent semantic clusters on both sides of the semantic fault are extracted. Based on the semantic embedding vector, expression motivation label, and event semantic role of the core expression segment, a semantic weight feature set is extracted as the semantic center of gravity node. The semantic bridge path across the fault is constructed based on the causal relationship and expression logic between the semantic center of gravity nodes.

[0029] During the construction process, if there is a semantic jump phenomenon, a historical expression template is introduced to generate a bridge node; the semantic feature information on both sides of the semantic fault is integrated, the financial concept unit is abstracted, and based on the subordinate, parallel or evolutionary relationship between the financial concept units, a conceptual organizational structure is constructed, with the bridge node as the root node, to generate a conceptual semantic tree with a hierarchical structure.

[0030] Preferably, the method for simulating the multi-directional propagation and deformation process of financial events includes:

[0031] Identify key event nodes in the concept semantic tree as the starting nodes of the semantic evolution path; based on the semantic tree structure relationship and contextual semantic tension, construct n semantic evolution paths oriented towards different semantic directions;

[0032] The event sequence in the semantic evolution path is structurally split and semantically rearranged to extract event units. Based on the causal, parallel and progressive relationships between events, the event sequence is reorganized according to the preset semantic reconstruction template to generate m semantic threads expressing different linguistic motivations. Each semantic thread reflects the evolution and deformation of financial events under the preset semantic framework.

[0033] Preferably, the method for generating a semantic multi-threaded evolution graph includes:

[0034] During the path construction process, a semantic deformation mechanism is introduced through word meaning replacement, syntactic transformation, expression motivation adjustment and semantic abstraction to generate semantic variant nodes and record semantic variant paths; all semantic threads are integrated and constructed into a semantic multi-threaded evolution graph containing main paths, side paths, semantic cross paths and semantic variant paths.

[0035] Preferably, the method for adjusting semantic fault determination and configuration mapping includes:

[0036] Extract each evolutionary path from the generated semantic multi-threaded evolutionary graph. Using a reverse mapping mechanism, map the abstract nodes in the evolutionary graph back to the actual semantic expressions and corresponding original text fragments. After the mapping is completed, align and compare the semantic expressions in the deduced paths with multi-source financial text data to identify semantic inconsistencies and chronological deviations.

[0037] Based on the mapping and comparison results, the actual deviation of the semantic evolution path is calculated, which is defined as the mean error between the predicted semantics and the true semantics of each event node. At the same time, the structural consistency polarization value is calculated to measure the deviation of the node connection relationship in the predicted semantic multi-threaded evolution graph and the true semantic multi-threaded evolution graph structure.

[0038] Based on the actual deviation of the semantic evolution path and the structural consistency polarization value, the semantic fault judgment threshold and configuration mapping parameters are dynamically adjusted through a collaborative error-driven parameter update mechanism to achieve continuous optimization and iteration between semantic recognition logic and text processing.

[0039] The technical effects and advantages of the network multi-source financial text big data processing method of the present invention are as follows:

[0040] Through the semantic distance calculation and tolerance threshold control of the configuration vector space, unified aggregation of different expression forms is achieved, which improves the ability to identify semantically consistent relationships under the diversity of language expressions; with the help of the joint modeling mechanism of expression motivation labels and configuration structures, not only the language form is taken into account, but also the pragmatic purpose behind the language is captured, making semantic clustering more in line with the event-driven nature of financial texts; by outputting configuration-equivalent semantic clusters and constructing transformation maps, it can support the stable deduction of subsequent semantic evolution paths and provide a highly consistent input source for simulating the semantic deformation process of financial events; using configuration aggregation as a bridge, without relying on predefined templates, structural bridging and dynamic merging between semantic fragments are achieved, which improves the overall understanding and expression ability of non-steady-state semantic changes in finance.

[0041] Through a reverse mapping mechanism, abstract nodes in the semantic multi-threaded evolution graph are mapped back to the original textual representation, identifying semantic expression inconsistencies and temporal misalignments, achieving deep alignment and error identification between the semantic modeling results and the original data. A dual error metric, the actual deviation of the semantic evolution path and the structural consistency polarization value, is proposed to achieve coordinated correction of semantic content and structure. A parameter update mechanism is designed to comprehensively consider both semantic and structural errors, dynamically adjusting key parameters, including the configuration mapping strategy and fault determination threshold, via gradient descent. This enables the system to self-regulate and self-correct during the actual semantic modeling process, adapting to the increasingly complex and changing financial semantic environment. A dynamic error feedback mechanism reduces modeling errors caused by semantic ambiguity, expression diversity, or structural mutations, improving the coherence of the logical chain between event nodes in the semantic graph and the rationality of the deduction path, thereby enhancing the interpretability and predictability of the semantic model. Through error-driven feedback optimization, key phenomena such as configurational structural jumps and semantic differentiation can be accurately captured and adjusted, thereby more accurately reflecting the evolutionary logic of financial event semantics. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flowchart of a method for processing multi-source financial text big data on the Internet;

[0043] Figure 2 This is a schematic diagram of the structure of a network multi-source financial text big data processing system;

[0044] Figure 3 This is a flow chart of the method for determining semantic faults provided by the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] Example 1

[0047] See also Figure 1 and Figure 3 As shown, Example 1 further illustrates a method for processing network multi-source financial text big data proposed by the present invention, including:

[0048] With the increasing diversification of financial information sources and the fragmentation of financial event dissemination, financial text big data exhibits characteristics such as multi-source heterogeneity, diverse structures, and complex expressions. To achieve timely perception and semantic understanding of financial market trends, deep semantic modeling and event evolution abstraction of multi-source financial texts on the Internet are urgently needed to support the development of intelligent analysis and decision-making support systems.

[0049] However, existing technologies still have the following technical limitations when processing such complex financial semantic data:

[0050] The ability of expression normalization is weak, and the ability of semantic consistency recognition is insufficient. Most existing methods rely on text clustering methods based on keyword extraction or shallow grammatical rules, which are difficult to effectively identify texts with large differences in expression forms but semantic consistency. Figure 1 The inconsistent financial text content leads to the decentralized representation of event information and the fragmentation of semantic views, which is not conducive to forming a unified analysis result.

[0051] The lack of a mechanism for modeling expression motivations makes it impossible to establish cross-structural semantic equivalence relationships. Traditional semantic analysis methods primarily focus on surface features at the syntactic or semantic level, failing to incorporate the implicit expression motivations or discourse intentions of financial language into the modeling system. This makes it difficult to establish effective equivalence relationships between text segments with significant structural differences, limiting the ability to achieve semantic aggregation and event fusion.

[0052] The construction logic of semantic evolution path is weak and the coherence of deduction is poor. In the multi-threaded evolution modeling of financial events, the existing methods lack a normalization mechanism based on the fusion of semantic structure and expression motivation, which leads to logical jumps and discontinuous connections in the event evolution path, seriously affecting the stability and credibility of semantic deduction.

[0053] The results of semantic structure construction lack a reverse feedback mechanism and cannot be continuously optimized. Current financial semantic modeling technology generally adopts the "one-way extraction + graph generation" paradigm, lacks the ability to reverse map the original text from the generated results, and cannot evaluate the deviation between the predicted structure and the actual semantics, resulting in quality problems such as logical breaks and structural drift in the semantic graph.

[0054] The lack of effective quantitative indicators for prediction errors and structural deviations makes model tuning difficult. Traditional methods lack systematic error metrics when faced with semantic path prediction errors and structural consistency deviations, and are unable to provide quantifiable feedback indicators, limiting the subsequent ability to fine-tune configuration mapping function optimization and fault determination strategies.

[0055] Configuration mapping and semantic fault determination rely on static thresholds and have poor adaptability. In existing systems, key parameters used to determine semantic faults and configuration similarity (such as semantic pointing difference thresholds and configuration distance thresholds) are mostly manually set and statically configured, and cannot dynamically adapt to changes in context and corpus, resulting in insufficient model generalization capabilities.

[0056] In order to effectively solve the above problems, the present invention proposes a method for processing network multi-source financial text big data, including:

[0057] S1. Collect multi-source financial text data, conduct semantic-level unstructured analysis, identify critical language points and triggering semantic breakpoints in semantic changes, construct a language trigger factor matrix for financial fluctuations, and capture key expression fragments;

[0058] S2. Receive the trigger factor matrix, use the language expression motivation modeling mechanism to extract the structure and perform configuration mapping on the key expression fragments, construct a configuration transformation diagram centered on the language expression motivation, and output configuration equivalent semantic clusters;

[0059] S3. Based on the configuration-equivalent semantic clusters, perform topological structure analysis, identify semantic fission points and mutation connections, determine semantic faults, and perform concept reconstruction on both sides of the fault to generate a conceptual semantic tree;

[0060] S4. Based on the key event nodes in the concept semantic tree, a semantic evolution path is constructed to simulate the multi-directional propagation and transformation process of financial events, and a semantic multi-threaded evolution graph is generated by event flow reorganization.

[0061] S5. Reversely map the deduction path of the semantic multi-threaded evolution graph, calculate the actual deviation of the semantic evolution path and the structural consistency polarization value, adjust the semantic fault judgment and configuration mapping, and realize continuous optimization and iteration between semantic recognition logic and text processing.

[0062] Methods for performing semantic-level unstructured analysis include:

[0063] Collect multi-source financial text data, including financial news data, policy announcement data, social media content data, corporate financial report text data, and analyst research reports; standardize the format of multi-source financial text data, remove redundant symbols, web page tags, and invalid content, and perform semantic-level unstructured analysis on the formatted financial text data;

[0064] Semantic-level unstructured analysis includes segmenting financial text data into semantic units, identifying financial entities, timing trigger words, sentiment words, and event expression structures; based on semantic unit segmentation, semantic annotation is performed through named entity recognition, part-of-speech tagging, and sentiment analysis, and a pre-trained language model is used to generate contextual embedding vectors.

[0065] Methods for capturing key expression fragments include:

[0066] Dynamic semantic difference analysis is performed on context embedding vectors to monitor the semantic evolution trajectory in the temporal structure and identify semantic segments in the context embedding vector that undergo jumps during the semantic expression process. A semantic change rate indicator is introduced to calculate the rate of change of the semantic vector difference between adjacent sentences or phrases to calibrate the semantic jump position during the semantic change process. This is identified as a language critical point, which refers to the expression node in natural language expression that may trigger a turning point in the understanding direction, semantic tendency, or event interpretation mode.

[0067] Analyze the expression structure before and after the language critical point, identify the expression paragraphs with sudden changes in syntactic structure and entity relationship, and define them as triggering semantic breakpoints; based on the language critical point and triggering semantic breakpoints, construct a financial volatility language trigger factor matrix, which uses key semantic segments as rows and semantic change features (such as polarity transition, timing offset, event turning probability) as columns to form a set of trigger factors for subsequent semantic clustering and configuration mapping; and perform expression importance weight sorting to screen out key expression segments whose expression importance weight is greater than the preset expression importance weight threshold.

[0068] Methods for structure extraction and configuration mapping include:

[0069] The system receives a financial volatility language trigger factor matrix, takes the key expression fragments contained in the financial volatility language trigger factor matrix as input, and extracts the language context, grammatical structure, and event element information corresponding to the fragments; based on the language expression motivation modeling mechanism, it constructs an expression motivation map for each key expression fragment. The expression motivation map is used to describe the driving intention, pragmatic logic, and semantic goals behind the language fragment, and is divided into types such as strategic expression, emotional expression, factual expression, or inferred expression based on language behavior characteristics;

[0070] Various expression motivations in the expression motivation map are classified and labeled, and grammatical structure extraction is performed. A combination of dependency syntax analysis, semantic role labeling and event trigger word recognition is used to extract the structural configuration of key expression fragments. The structural configuration includes core predicates, subject and object entities, modifying structures and emotional regulatory factors; based on the structural configuration, a language configuration mapping table is constructed, and key expression fragments with similar expression motivations are structurally mapped and classified into a unified configuration category.

[0071] Methods for outputting configurationally equivalent semantic clusters include:

[0072] For each key expression segment, a grammatical configuration mapping function is used to map it into a grammatical configuration vector. The grammatical configuration vector comprehensively represents the syntactic structure features, semantic role, and expression motivation label of the key expression segment. Taking a specific central expression segment as a benchmark, the semantic configuration distance between the key expression segment and the specific central expression segment in the configuration vector space is calculated using cosine similarity.

[0073] The semantic configuration distance is used to measure the similarity between two key expression fragments in terms of structure and semantic expression motivation. A configuration equivalence tolerance threshold is preset, and all key expression fragments that meet the configuration equivalence semantic cluster judgment relationship are classified into the same configuration equivalence semantic cluster. With each configuration equivalence semantic cluster as a node, a configuration transformation graph with language expression motivation as the axis is constructed.

[0074] The determination relationship of configurational equivalence semantic clusters is: ;in, Indicates a key expression fragment Configuration-equivalent semantic clusters constructed for the center; Indicates the Key expression fragments; Indicates a specific central expression segment; Indicates that the key expression fragment The result of the mapping function mapped to the grammatical configuration vector space; Indicates key expression fragments with specific central expression fragments Semantic configuration distance in configuration vector space; Indicates the preset configuration equivalence tolerance threshold;

[0075] The following problems existing in the existing technology are solved: in the existing technology, most of the text clustering methods based on keywords or shallow grammatical rules are used, which makes it difficult to accurately capture financial text content with the same expression intention but large differences in form, resulting in limited semantic consistency recognition capabilities; traditional methods lack modeling of expression motivations and cannot establish equivalence relationships between text fragments with diverse structures, causing the same events or semantics to be fragmented during the analysis process, making it difficult to form a complete semantic view; there is a lack of expression normalization mechanism based on the fusion of structural configuration and expression motivation, resulting in discontinuous path logic and unstable deduction pathways in the process of financial semantic evolution modeling.

[0076] Compared with the existing technology, the beneficial effects are: through the semantic distance calculation and tolerance threshold control of the configuration vector space, unified aggregation of different expression forms is achieved, and the ability to recognize semantic consistency relationships under the diversity of language expression is improved; with the help of the joint modeling mechanism of expression motivation labels and configuration structures, not only the language form is taken into account, but also the pragmatic purpose behind the language is captured, making semantic clustering more in line with the event-driven nature of financial texts; by outputting configuration-equivalent semantic clusters and constructing transformation graphs, it can support the stable deduction of subsequent semantic evolution paths, and provide a highly consistent input source for simulating the semantic deformation process of financial events; compared with existing isolated keywords or shallow context matching methods, the present invention uses configuration aggregation as a bridge, and without relying on predefined templates, it realizes structural bridging and dynamic merging between semantic fragments, thereby improving the system's overall understanding and expression capabilities of financial non-steady-state semantic changes.

[0077] Methods for determining semantic faults include:

[0078] The configuration-equivalent semantic clusters are regarded as graph nodes, and a semantic structure graph is constructed. The edges in the semantic structure graph represent the semantic connection relationship between different configuration-equivalent semantic clusters. A topological structure analysis is performed on the constructed semantic structure graph. Nodes with an increased number of outgoing edges and a semantic pointing distribution difference greater than a preset semantic pointing distribution difference threshold are identified as semantic fission points. Connecting edges with a configuration vector distance greater than a preset configuration vector distance threshold are identified as mutation connections. The information of semantic fission points and mutation connections is integrated to determine the semantic fault area.

[0079] Methods for generating concept semantic trees include:

[0080] The core expression segments in the configurationally equivalent semantic clusters on both sides of the semantic fault are extracted. Based on the semantic embedding vector, expression motivation label, and event semantic role of the core expression segment, a semantic weight feature set is extracted as the semantic center of gravity node. The semantic bridge path across the fault is constructed based on the causal relationship and expression logic between the semantic center of gravity nodes.

[0081] During the construction process, if there is a semantic jump phenomenon, a historical expression template is introduced to generate a bridge node; the semantic feature information on both sides of the semantic fault is integrated, the financial concept unit is abstracted, and based on the subordinate, parallel or evolutionary relationship between the financial concept units, a conceptual organizational structure is constructed, with the bridge node as the root node, to generate a conceptual semantic tree with a hierarchical structure.

[0082] Methods for simulating the multi-directional propagation and transformation of financial events include:

[0083] Identify key event nodes in the concept semantic tree as the starting nodes of the semantic evolution path; key event nodes include semantic trunk nodes, semantic transition nodes, event conflict nodes, and emotional polarization nodes;

[0084] Semantic trunk nodes are located on the main path of the concept semantic tree, connecting multiple important subnodes and fulfilling the key function of the semantic transmission chain. They often correspond to core financial events (such as "interest rate hike," "default," and "policy adjustment"). Semantic transition nodes have many branches and can differentiate semantic directions (such as "although...but" and "due to..."), often triggering changes in semantic direction.

[0085] Event conflict nodes represent event expression units that are characterized by public opinion controversy, market disagreement, or confrontational expressions, such as "rating upgrades coexisting with financial report declines," and constitute a potential source of semantic tension. Emotional polarization nodes, such as "plunge," "bad news," and "boost," carry clear positive or negative sentiment, and are common hot spots in financial texts that trigger widespread dissemination.

[0086] Based on the semantic tree structure relationship and contextual semantic tension, n semantic evolution paths oriented towards different semantic directions are constructed respectively; the semantic tree structure relationship includes subordination relationship, parallel relationship, causal relationship and temporal sequence relationship; the contextual semantic tension includes semantic inconsistency, logical jump or break, information expectation gap and contrastive expression;

[0087] Semantic inconsistency: For example, the emotional polarity in the previous and subsequent sentences is different, which creates semantic tension (such as "The stock market plummeted, but experts say there is no need to panic"); logical jumps or breaks: There are implicit omissions or turns between the contexts, which make the semantic transition tense or abrupt; information expectation gap: There is a strong "information compensation demand" in the context, that is, the expression is incomplete or leaves suspense, which triggers reasoning tension; contrasting expressions: such as structures such as "although...but..." and "although...however...", which have a tension mechanism that drives the evolution of semantic direction.

[0088] The event sequence in the semantic evolution path is structurally split and semantically rearranged to extract event units. Based on the causal, parallel and progressive relationships between events, the event sequence is reorganized according to the preset semantic reconstruction template to generate m semantic threads expressing different linguistic motivations. Each semantic thread reflects the evolution and deformation of financial events under the preset semantic framework.

[0089] The method for generating a semantic multithreaded evolution graph includes:

[0090] During the path construction process, a semantic deformation mechanism is introduced through word meaning replacement, syntactic transformation, expression motivation adjustment and semantic abstraction to generate semantic variant nodes and record semantic variant paths; all semantic threads are integrated and constructed into a semantic multi-threaded evolution graph containing main paths, side paths, semantic cross paths and semantic variant paths.

[0091] Methods for adjusting semantic fault determination and configuration mapping include:

[0092] Extract each evolutionary path from the generated semantic multi-threaded evolutionary graph. Using a reverse mapping mechanism, map the abstract nodes in the evolutionary graph back to the actual semantic expressions and corresponding original text fragments. After the mapping is completed, align and compare the semantic expressions in the deduced paths with multi-source financial text data to identify semantic inconsistencies and chronological deviations.

[0093] Based on the mapping and comparison results, the actual deviation of the semantic evolution path is calculated, which is defined as the mean error between the predicted semantics and the true semantics of each event node;

[0094] The actual deviation is: ;in, Indicates that the system The predicted semantics of each event node; Represents true semantics; Indicates the total number of event nodes; Indicates the index of the event node;

[0095] At the same time, the structural consistency polarization value is calculated to measure the deviation of the node-to-node connection relationship between the predicted semantic multithreaded evolution graph and the real semantic multithreaded evolution graph structure;

[0096] The structural consistency polarization value is: ;in, Represents a set of node pairs in a semantic multithreaded evolution graph; is a node pair, indicating the connection relationship between nodes; In the adjacency matrix representing the predicted semantic multithreaded evolution graph, the node With node Whether the connected value exists; In the adjacency matrix representing the real semantic multithreaded evolution graph, the node With node The actual connection value; and Indicates the index of the node;

[0097] Based on the actual deviation of the semantic evolution path and the structural consistency polarization value, the semantic fault judgment threshold and configuration mapping parameters are dynamically adjusted through a collaborative error-driven parameter update mechanism to achieve continuous optimization and iteration between semantic recognition logic and text processing.

[0098] It should be noted that the adjustment of the semantic fault determination threshold here includes a preset semantic pointing distribution difference threshold and a preset configuration vector distance threshold;

[0099] The collaborative error-driven parameter update mechanism is: ;in, Indicates the Configuration mapping parameter values ​​after iteration update; Indicates the Configuration mapping parameter values ​​at iterations; Represents the learning rate, which is used to control the amplitude of the configuration mapping parameter update; Indicates the actual deviation of the semantic evolution path; Represents the structural polarization control factor, which is used to adjust the influence weight of structural deviation in joint optimization; Indicates the structural consistency polarization value;

[0100] The following problems existing in the existing technology are solved: Existing financial semantic modeling technologies mostly use one-way extraction and graph construction methods, which cannot systematically evaluate and provide feedback on the matching between the constructed semantic structure and the original financial text, resulting in problems such as logical breaks and structural drift in the semantic graph, affecting the accuracy and completeness of the final semantic modeling. In multi-threaded semantic evolution scenarios, the predicted structure often deviates from the actual semantic evolution logic. Existing technologies lack precise definitions and measurement methods for semantic prediction errors and structural consistency errors, and cannot provide effective indicator support for subsequent parameter optimization, resulting in the inability to effectively evolve the system's semantic understanding capabilities. Traditional configuration mapping functions and semantic fault judgments rely on static threshold settings (such as semantic pointing distribution difference thresholds, configuration vector distance thresholds, etc.), which are difficult to maintain stable effects under different corpora or contexts, and have problems such as poor adaptability and high misjudgment rates.

[0101] Compared with existing technologies, this system offers significant advantages: Through a reverse mapping mechanism, abstract nodes in the semantic multi-threaded evolution graph are mapped back to their original textual representations, identifying semantic expression inconsistencies and temporal misalignments, achieving deep alignment and error identification between the semantic modeling results and the original data. A dual error metric, the actual deviation of the semantic evolution path and the structural consistency polarization value, is proposed to achieve coordinated correction of semantic content and structure. A parameter update mechanism comprehensively considers both semantic and structural errors, dynamically adjusting key parameters, including the configuration mapping strategy and fault determination threshold, via gradient descent. This enables the system to self-regulate and self-correct during the actual semantic modeling process, adapting to the increasingly complex and changing financial semantic environment. A dynamic error feedback mechanism reduces modeling errors caused by semantic ambiguity, expression diversity, or structural mutations, improving the coherence of the logical chain between event nodes in the semantic graph and the rationality of the deduction path, thereby enhancing the interpretability and predictability of the semantic model. Through error-driven feedback optimization, key phenomena such as configurational structural jumps and semantic differentiation can be accurately captured and adjusted, thereby more accurately reflecting the evolutionary logic of financial event semantics.

[0102] The preset expression importance weight threshold is set by the staff. By collecting different expression importance weights, the average value of multiple expression importance weights is taken as the preset expression importance weight threshold. Similarly, the preset configuration equivalence tolerance threshold, the preset semantic pointing distribution difference threshold, and the preset configuration vector distance threshold are set.

[0103] This embodiment achieves unified aggregation of different expression forms through semantic distance calculation and tolerance threshold control in the configuration vector space, thereby improving the ability to identify semantically consistent relationships under the diversity of language expressions; with the help of the joint modeling mechanism of expression motivation labels and configuration structures, it not only takes into account the language form, but also captures the pragmatic purpose behind the language, making semantic clustering more in line with the event-driven nature of financial texts; by outputting configuration-equivalent semantic clusters and constructing transformation graphs, it can support the stable deduction of subsequent semantic evolution paths, providing a highly consistent input source for simulating the semantic deformation process of financial events; using configuration aggregation as a bridge, without relying on predefined templates, it realizes structural bridging and dynamic merging between semantic fragments, thereby improving the overall understanding and expression ability of non-steady-state semantic changes in finance.

[0104] Through a reverse mapping mechanism, abstract nodes in the semantic multi-threaded evolution graph are mapped back to the original textual representation, identifying semantic expression inconsistencies and temporal misalignments, achieving deep alignment and error identification between the semantic modeling results and the original data. A dual error metric, the actual deviation of the semantic evolution path and the structural consistency polarization value, is proposed to achieve coordinated correction of semantic content and structure. A parameter update mechanism is designed to comprehensively consider both semantic and structural errors, dynamically adjusting key parameters, including the configuration mapping strategy and fault determination threshold, via gradient descent. This enables the system to self-regulate and self-correct during the actual semantic modeling process, adapting to the increasingly complex and changing financial semantic environment. A dynamic error feedback mechanism reduces modeling errors caused by semantic ambiguity, expression diversity, or structural mutations, improving the coherence of the logical chain between event nodes in the semantic graph and the rationality of the deduction path, thereby enhancing the interpretability and predictability of the semantic model. Through error-driven feedback optimization, key phenomena such as configurational structural jumps and semantic differentiation can be accurately captured and adjusted, thereby more accurately reflecting the evolutionary logic of financial event semantics.

[0105] Example 2

[0106] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of Example 1. A network multi-source financial text big data processing system is provided, including:

[0107] The non-steady-state financial analysis module collects multi-source financial text data and performs semantic-level unstructured analysis to identify critical language points and triggering semantic breakpoints in semantic changes, construct a matrix of language trigger factors for financial fluctuations, and capture key expression fragments.

[0108] The language mapping and reorganization module receives the trigger factor matrix, adopts the language expression motivation modeling mechanism, performs structure extraction and configuration mapping on key expression fragments, constructs a configuration transformation graph centered on the language expression motivation, and outputs configuration-equivalent semantic clusters.

[0109] The financial concept reconstruction module performs topological structure analysis based on configuration-equivalent semantic clusters, identifies semantic fission points and mutation connections, determines semantic faults, and performs concept reconstruction on both sides of the fault to generate a concept semantic tree.

[0110] The semantic evolution modeling module constructs a semantic evolution path based on key event nodes in the concept semantic tree, simulates the multi-directional propagation and transformation process of financial events, and generates a semantic multi-threaded evolution graph using event flow reorganization.

[0111] A self-consistent feedback loop generation module reversely maps the deduction path of the semantic multi-threaded evolution graph, calculates the actual deviation of the semantic evolution path and the structural consistency polarization value, adjusts the semantic fault judgment and configuration mapping, and realizes continuous optimization and iteration between semantic recognition logic and text processing.

[0112] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0113] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for processing network multi-source financial text big data, characterized by: include: S1. Collect multi-source financial text data, conduct semantic-level unstructured analysis, identify critical language points and triggering semantic breakpoints in semantic changes, construct a language trigger factor matrix for financial fluctuations, and capture key expression fragments; S2. Receive the trigger factor matrix, use the language expression motivation modeling mechanism to extract the structure and perform configuration mapping on the key expression fragments, construct a configuration transformation diagram centered on the language expression motivation, and output configuration equivalent semantic clusters; The method for outputting configuration-equivalent semantic clusters includes: For each key expression segment, a grammatical configuration mapping function is used to map it into a grammatical configuration vector. The grammatical configuration vector comprehensively represents the syntactic structure features, semantic role, and expression motivation label of the key expression segment. Taking the central expression segment as the benchmark, the semantic configuration distance between the key expression segment and the central expression segment in the configuration vector space is calculated using cosine similarity. The semantic configuration distance is used to measure the similarity between two key expression segments in terms of structure and semantic expression motivation. A configuration equivalence tolerance threshold is preset, and all key expression segments that meet the configuration equivalence semantic cluster judgment relationship are classified into the same configuration equivalence semantic cluster. Each configuration equivalence semantic cluster is used as a node to construct a configuration transformation graph centered on the language expression motivation. S3. Based on the configuration-equivalent semantic clusters, perform topological structure analysis, identify semantic fission points and mutation connections, determine semantic faults, and perform concept reconstruction on both sides of the fault to generate a conceptual semantic tree; S4. Based on the key event nodes in the concept semantic tree, a semantic evolution path is constructed to simulate the multi-directional propagation and transformation process of financial events, and a semantic multi-threaded evolution graph is generated by event flow reorganization. S5. Reversely map the deduction path of the semantic multi-threaded evolution graph, calculate the actual deviation of the semantic evolution path and the structural consistency polarization value, adjust the semantic fault judgment and configuration mapping, and realize continuous optimization and iteration between semantic recognition logic and text processing.

2. A method for processing network multi-source financial text big data according to claim 1, characterized in that: The method for performing semantic-level unstructured analysis includes: Collect multi-source financial text data, including financial news data, policy announcement data, social media content data, corporate financial report text data, and analyst research reports; standardize the format of multi-source financial text data, remove redundant symbols, web page tags, and invalid content, and perform semantic-level unstructured analysis on the formatted financial text data; Semantic-level unstructured analysis includes segmenting financial text data into semantic units, identifying financial entities, timing trigger words, sentiment words, and event expression structures; based on semantic unit segmentation, semantic annotation is performed through named entity recognition, part-of-speech tagging, and sentiment analysis, and a pre-trained language model is used to generate contextual embedding vectors.

3. A method for processing network multi-source financial text big data according to claim 2, characterized in that: The method for capturing key expression fragments comprises: Dynamic semantic difference analysis is performed on context embedding vectors to monitor the semantic evolution trajectory in the temporal structure and identify semantic segments in the context embedding vector that undergo jumps during the semantic expression process. A semantic change rate indicator is introduced to calculate the rate of change of the semantic vector difference between adjacent sentences or phrases to calibrate the semantic jump position during the semantic change process and identify it as a language critical point. Analyze the expression structure before and after the language critical point, identify the expression paragraphs with mutations in syntactic structure and entity relationship, and define them as triggering semantic breakpoints; construct the financial volatility language trigger factor matrix based on the language critical point and triggering semantic breakpoints; and perform expression importance weight sorting to screen out key expression fragments whose expression importance weight is greater than the preset expression importance weight threshold.

4. A method for processing network multi-source financial text big data according to claim 3, characterized in that: The method for performing structure extraction and configuration mapping comprises: The method receives a financial volatility language trigger factor matrix, takes key expression segments contained in the financial volatility language trigger factor matrix as input, extracts the language context, grammatical structure, and event element information corresponding to the segments; and constructs an expression motivation map for each key expression segment based on the language expression motivation modeling mechanism. The various expression motivations in the expression motivation map are classified and labeled, and grammatical structure extraction is performed. The structural configuration of key expression fragments is extracted by combining dependency syntax analysis, semantic role labeling and event trigger word recognition. Based on the structural configuration, a language configuration mapping table is constructed, and key expression fragments with similar expression motivations are structurally mapped and classified into a unified configuration category.

5. A method for processing network multi-source financial text big data according to claim 4, characterized in that: The method for determining semantic faults includes: The configuration-equivalent semantic clusters are regarded as graph nodes, and a semantic structure graph is constructed. The edges in the semantic structure graph represent the semantic connection relationship between different configuration-equivalent semantic clusters. A topological structure analysis is performed on the constructed semantic structure graph. Nodes with an increased number of outgoing edges and a semantic pointing distribution difference greater than a preset semantic pointing distribution difference threshold are identified as semantic fission points. Connecting edges with a configuration vector distance greater than a preset configuration vector distance threshold are identified as mutation connections. The information of semantic fission points and mutation connections is integrated to determine the semantic fault area.

6. A method for processing network multi-source financial text big data according to claim 5, characterized in that: The method for generating a concept semantic tree comprises: The core expression segments in the configurationally equivalent semantic clusters on both sides of the semantic fault are extracted. Based on the semantic embedding vector, expression motivation label, and event semantic role of the core expression segment, a semantic weight feature set is extracted as the semantic center of gravity node. The semantic bridge path across the fault is constructed based on the causal relationship and expression logic between the semantic center of gravity nodes. During the construction process, if there is a semantic jump phenomenon, a historical expression template is introduced to generate a bridge node; the semantic feature information on both sides of the semantic fault is integrated, the financial concept unit is abstracted, and based on the subordinate, parallel or evolutionary relationship between the financial concept units, a conceptual organizational structure is constructed, with the bridge node as the root node, to generate a conceptual semantic tree with a hierarchical structure.

7. A method for processing network multi-source financial text big data according to claim 6, characterized in that: The method for simulating the multi-directional propagation and deformation process of financial events includes: Identify key event nodes in the concept semantic tree as the starting nodes of the semantic evolution path; based on the semantic tree structure relationship and contextual semantic tension, construct n semantic evolution paths oriented towards different semantic directions; The event sequence in the semantic evolution path is structurally split and semantically rearranged to extract event units. Based on the causal, parallel and progressive relationships between events, the event sequence is reorganized according to the preset semantic reconstruction template to generate m semantic threads expressing different linguistic motivations. Each semantic thread reflects the evolution and deformation of financial events under the preset semantic framework.

8. A method for processing network multi-source financial text big data according to claim 7, characterized in that: The method for generating a semantic multi-threaded evolution graph includes: During the path construction process, a semantic deformation mechanism is introduced through word meaning replacement, syntactic transformation, expression motivation adjustment and semantic abstraction to generate semantic variant nodes and record semantic variant paths; all semantic threads are integrated and constructed into a semantic multi-threaded evolution graph containing main paths, side paths, semantic cross paths and semantic variant paths.

9. A method for processing network multi-source financial text big data according to claim 8, characterized in that: The method for adjusting semantic fault determination and configuration mapping includes: Extract each evolutionary path from the generated semantic multi-threaded evolutionary graph. Using a reverse mapping mechanism, map the abstract nodes in the evolutionary graph back to the actual semantic expressions and corresponding original text fragments. After the mapping is completed, align and compare the semantic expressions in the deduced paths with multi-source financial text data to identify semantic inconsistencies and chronological deviations. Based on the mapping and comparison results, the actual deviation of the semantic evolution path is calculated, which is defined as the mean error between the predicted semantics and the true semantics of each event node. At the same time, the structural consistency polarization value is calculated to measure the deviation of the node connection relationship in the predicted semantic multi-threaded evolution graph and the true semantic multi-threaded evolution graph structure. Based on the actual deviation of the semantic evolution path and the structural consistency polarization value, the semantic fault judgment threshold and configuration mapping parameters are dynamically adjusted through a collaborative error-driven parameter update mechanism to achieve continuous optimization and iteration between semantic recognition logic and text processing.

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