94 results about "Cognitive computing" patented technology

The invention provides a streaming query semantic map adaptive enhancement method and system based on cognitive computing, and relates to the technical field of semantic maps, comprising the steps of computing node affinity through a map attention mechanism, identifying knowledge clusters by using a community discovery algorithm, performing knowledge deduction by taking the knowledge clusters as a basic unit, and predicting and complementing potential semantic relationships. And after multi-agent reinforcement learning optimization verification, outputting an enhanced semantic map and a credibility score table. And deploying the enhanced atlas to an edge network, carrying out parallel semantic understanding and query optimization suggestion generation, integrating optimization suggestions through federal learning, updating the central cognitive model, and distributing the central cognitive model to the edge network. According to the method, the semantic map is enhanced through cognitive calculation, so that more accurate semantic understanding and more efficient query optimization are realized.

A system and method for persistent cognitive computation with temporally synchronized multimodal processing implements a geometric approach to artificial intelligence through typed latent entities within a dynamic manifold substrate. The system maintains a latent manifold incorporating heterogeneous data modalities where local curvature reflects semantic density and typed entities are stratified according to structural properties. Temporal synchronization coordinates asynchronous multimodal data streams through generation of temporal alignment fields within the manifold that preserve semantic coherence across modal boundaries. Type-aware geometric operations enforce operation legality based on entity type and local manifold geometry, enabling structured recombination, compression, and traversal while preventing semantic distortion. The system executes synchronized manifold reorganization during idle periods through coordinated optimization operations including perturbation analysis and topological surgery. This architecture enables persistent memory through geometric encoding where frequently accessed concepts develop high-curvature regions and cognitive patterns emerge from usage-based manifold evolution.

Computer-implemented methods and systems are disclosed for Information Technology Service Management (ITSM). The pioneering AI-driven system revolutionizes IT service management by uniquely validating, suggesting, and inferencing incident and problem data. At its core are cutting-edge generative AI techniques like GANs and LLMs, requiring intricate training and iterative refinement on varied data sets, showcasing a depth of expertise in database structures and AI concepts. It bridges critical gaps in incident resolution and classification through cognitive computing, AI, NLP, and deep learning, applied to both historical and current data. The system comprises modules for Incident Validation & Classification, Resolution Validation, Generative Intelligence, Problem Probability Calculation, and Prevention Recommendation, each employing AI to enhance standard compliance, predictive analysis, and proactive management, thereby setting new standards for IT service management efficiency and effectiveness.

The invention discloses intelligent knowledge service electronic equipment for information retrieval, and the equipment comprises a data management layer which is responsible for providing original data for a cognitive calculation layer, and carrying out the closed-loop optimization through continuously monitoring the feedback of a man-machine interaction layer; the cognitive calculation layer is responsible for converting original data into a semantic tag system and a dynamic knowledge graph, applying BERT + GNN hybrid analysis, BERT processing text context and GNN analysis entity relationship in the knowledge graph, and dynamically adjusting a reasoning path according to user field preference by combining an SWRL rule engine and reinforcement learning; the service arrangement layer is responsible for extracting data from the multi-modal data pool, solidifying and arranging a complex query stream to perform logic decoupling and efficient multiplexing, and providing a visual interface to support non-technical personnel to configure a new scene; and the man-machine interaction layer is used for constructing a natural interaction bridge through the structured answers and the visual maps output by the service arrangement layer, carrying out personalized interface adaptation based on historical dialogues, and establishing a user feedback closed loop.

The invention provides a learning data accurate mining method based on cognitive calculation driving. The learning data accurate mining method comprises the following steps of S1, performing multi-modal learning behavior data acquisition and heterogeneous integration; s2, a dynamic feature weight optimization step based on calculus; s3, performing cognitive state differential equation modeling; s4, a cognitive diagnosis hybrid model based on statistics; s5, incremental construction of the dynamic knowledge graph is carried out; s6, constructing a federated learning framework for privacy protection; s7, a cognitive intervention strategy is generated; s8, constructing a multi-granularity effect evaluation system; s9, a step of constructing an interpretability enhancement module; s10, a step of carrying out adaptive iterative optimization; the learning data accurate mining method based on cognitive calculation driving has the following advantages that the data utilization rate breaks through the limitation of a traditional method through federated learning and heterogeneous graph fusion; the attention prediction error is reduced through differential equation modeling, and the method is superior to all existing ARIMA / LSTM baseline models.

The invention provides a multi-modal education evaluation system and method based on super-automation and federal cognitive calculation, and the system integrates multi-source heterogeneous data collection, a federal cognitive calculation engine, an automatic evaluation workflow engine and a privacy enhanced data processing architecture. And comprehensive acquisition, efficient processing, accurate evaluation and safety protection of education data are realized. Through the LSTM-Bayesian hybrid model of dynamic parameter adjustment, intelligent feature engineering, interpretability analysis, reinforcement learning strategy optimization and other technologies, the system can generate personalized learning path planning and an adaptive teaching intervention scheme, and the teaching effect and learning efficiency are improved. And meanwhile, the security and privacy of the education data are ensured by adopting privacy protection technologies such as differential privacy and homomorphic encryption.

The invention discloses an AI-based enterprise benefit policy intelligent matching and declaration auxiliary system, and relates to the technical field of policy declaration auxiliary systems. The data collection module collects policy and enterprise data in a multi-modal fusion manner, and dynamically updates according to activeness; the preprocessing module performs cleaning through a high-performance cluster, analyzes a policy through sentiment analysis and performs AES encryption; the feature extraction module uses GAN enhancement and cognitive calculation mining to update a knowledge graph; the matching calculation module calculates the matching degree in combination with a complex network, and the declaration auxiliary module demonstrates the process through VR and AR and answers questions through intelligent customer service. The risk assessment module assesses the risk by using a block chain and the like; the user interaction module supports a brain-computer interface and the like and adaptively adjusts the interface; and the data updating and feedback module records feedback by means of the block chain and optimizes a prediction mechanism. The system is advantaged in that multi-channel accurate acquisition is facilitated, enterprise matching policy bonus is facilitated, a declaration link is simplified, the cost is reduced, data authenticity is guaranteed, cooperation is promoted, continuous optimization is carried out according to feedback, and enterprise benefit policy landing is promoted.

Systems and methods are provided for training an artificial intelligence system and generating audible content for output. The method utilizing a system including at least an application plane layer, a control plane layer including a cognitive computing unit, the cognitive computing unit using at least machine learning for training of the cognitive computing unit, a training input to the system including an input for receiving content for training during the machine learning, and a data plane layer, the data plane layer including an input interface to receive and store data input content from one or more data sources other than the control plane layer, the data input content being subject to transformation into audible content for output. Data input content information is used in synthesizing audible output content at least in part by transforming the data input content into the audible output content.

The present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The invention belongs to the field of data analysis and evaluation of decision-making schemes, the field of machine learning and cognitive calculation and the field of intelligent decision-making, and relates to a subjective and objective dynamic balance seaborne-to-sea decision-making scheme evaluation method fusing intuitionistic fuzzy. The method comprises the following steps: firstly, simulating a subjective dimension by utilizing an intuitionistic fuzzy set IFSs to obtain an auxiliary quantitative index; secondly, accurately calculating objective weights of the quantitative indexes and the auxiliary indexes thereof by adopting an entropy weight method; the subjective weight of the qualitative index is calculated by using the Bayesian network, so that the interpretability and accuracy of subjective judgment are ensured; and finally, organically combining subjective and objective weights through a dynamic combination mode, and calculating a comprehensive score of the sea-to-sea decision scheme. The method not only overcomes the defect that subjective factors are too heavy in traditional evaluation, but also fully considers objective actual conditions, and can realize scientific and comprehensive evaluation of the sea-to-sea decision scheme.

The invention discloses a multi-parameter cooperative control method for non-excavation repair of a drainage pipe network in urban updating, and relates to the technical field of municipal engineering, and the method comprises the steps: firstly constructing a quantum entanglement sensing pipe network digital twinborn model, fusing multi-source data, monitoring micro-strain in real time through a nano sensor, deploying a self-organizing intelligent node and terahertz imaging equipment, and constructing a quantum entanglement sensing pipe network digital twinborn model; multi-dimensional features are extracted, repair parameters are dynamically regulated and controlled through technologies such as magnetofluid plugging and multi-robot cooperation based on digital twinning virtual simulation, finally, the repair effect is evaluated and the scheme is optimized in combination with cognitive calculation and microbial self-healing technologies, and full-process intelligent control is achieved. The intelligent level of non-excavation repair of the drainage pipe network is improved, the problem that traditional monitoring is one-sided is solved through high-precision data fusion, precise construction control is achieved through cooperation of magnetofluid leaking stoppage and the robot, durability is enhanced through the microorganism self-healing technology, repair quality and efficiency are improved, and urban updating and sustainable development are promoted.

The invention discloses an unmanned aerial vehicle path optimization method based on a bionic growth optimization process. The method comprises the following steps: establishing an unmanned aerial vehicle perception model; establishing an unmanned aerial vehicle control model; and unmanned aerial vehicle path planning based on a bionic growth optimization process. According to the invention, artificial intelligence, machine learning, cognitive calculation and simulation of an event-related potential loop development mode of a human brain are fused. Establishing a sensing model, and enabling the unmanned aerial vehicle to sense and recognize surrounding obstacle information; establishing a control model, allowing the unmanned aerial vehicle to select eight basic flight directions on a two-dimensional plane, and forming an obstacle avoidance strategy; path planning is combined with an artificial potential field method and a bionic growth mechanism, so that the unmanned aerial vehicle perceives obstacles in real time and optimizes a navigation path in the flight process; along with the accumulation of flight experience, the unmanned aerial vehicle knowledge base is continuously updated, and a more efficient and safer path plan is formed. According to the invention, the adaptability and decision ability of the unmanned aerial vehicle in an unknown environment are significantly improved, and the autonomous obstacle avoidance ability and task execution efficiency of the unmanned aerial vehicle in a complex environment are improved.

The invention discloses a heterogeneous feature fusion-based multi-task traffic situation cognitive calculation method, and belongs to the field of intelligent traffic. The method specifically comprises the following steps: firstly, preprocessing multi-source traffic observation data to obtain normalized traffic flow, OD raster data and traffic state data; then, for a current frame, carrying out dynamic space-time convolution and diffusion diagram convolution on traffic flow and OD raster data to obtain node-level space-time features; meanwhile, carrying out feature extraction on the traffic state data by utilizing 3D convolution to obtain grid-level spatial-temporal features; and then, inputting the features and the common features into a heterogeneous cross attention fusion module to obtain a unified fusion feature h of the current frame, performing multi-task prediction, and respectively obtaining flow, OD and traffic state prediction results of future K steps. And finally, inputting the prediction result of each task and a true value to carry out multi-task loss calculation, carrying out parameter updating and completing model training. According to the invention, multi-task prediction can be realized by using multiple prediction heads.

The invention discloses a complex information chain modeling and self-adaptive reconstruction method based on cognitive calculation. The method comprises the steps that an information chain topology model is constructed according to nodes and links, and dynamic state parameters are configured; performing network cognitive evaluation based on the topology model and the state parameters, and generating a node efficiency index and a link reliability index; when a link failure is detected, determining an alternative path in the topology in which the failed link is removed based on the index; and according to the alternative path, reconstructing a topology model and updating parameters, and triggering new cognitive evaluation to form an adaptive closed loop. According to the method, the dynamic cognition and vulnerability analysis of the complex information chain can be realized, the adaptive reconstruction can be quickly completed when the link fails, and the robustness and task guarantee capability of the information chain in a dynamic confrontation environment can be remarkably improved.

The invention relates to the technical field of automatic driving, and discloses a Robotaxi-oriented VLA-world model fusion automatic driving system and a closed-loop training platform, and the system comprises an environment perception coding layer which is used for generating space-time aligned environment feature vectors; the mixed cognitive calculation core layer integrates a reactive sub-network, a judicial sub-network, a cognitive uncertainty evaluator and an anti-fact query interface; wherein the reactive sub-network is used for generating future trajectory distribution, and the judicial sub-network is used for calling the reactive sub-network to perform future state simulation through an anti-fact query interface when a trajectory distribution entropy value exceeds a threshold value, and executing thinking chain reasoning based on a simulation result to generate a control instruction. According to the method, deep closed loop of reactive prediction and judicial reasoning is realized through an anti-fact query mechanism, and the problems of cognitive splitting and reasoning short view existing when a traditional modular system processes a long-tail scene are solved.

The invention relates to the technical field of education and teaching. The invention provides a classroom teaching dynamic feedback and evaluation system integrated with multi-modal emotion calculation. The system comprises a multi-modal data acquisition module used for acquiring multi-source data in a classroom teaching environment and sending the multi-source data to an emotion-cognitive calculation center; the emotion-cognition calculation center is used for carrying out fusion analysis on the multi-source data, identifying the emotion state and the cognition state of the student, carrying out attribution analysis through an emotion-cognition coupling model, generating a coupling state label for describing the specific reason of the learning dilemma, and sending the coupling state label to the dynamic intervention engine; the dynamic intervention engine is used for matching and generating individual-level, group-level and system-level teaching intervention instructions from the teaching strategy knowledge base according to the received coupling state labels; and the visual feedback and evaluation module is connected with the emotion-cognition calculation center and the dynamic intervention engine, and is used for providing real-time classroom state visual display for the teacher and pushing personalized learning suggestions to the student terminal.

The invention provides a multi-agent collaborative cognitive calculation method based on point cloud feature marking, and belongs to the field of Internet of Vehicles. The method specifically comprises the following steps: firstly, inputting original point cloud data collected by a sensor of a vehicle intelligent agent into a point cloud feature mark generator, converting the original point cloud data into a one-dimensional point cloud feature mark sequence semantic perception dynamic encoder for encoding a mark sequence, and generating a feature sequence; packaging the feature sequence, the space coordinates and the pose information of the self-vehicle intelligent body into a message data packet; converting the feature mark coordinate space of the neighbor intelligent body into a coordinate system with the self-vehicle intelligent body as the center by a point cloud mark aggregation module, and generating a unified sequence; and the semantic perception dynamic fusion module carries out global context modeling and dynamic fusion on the unified sequence, corrects feature mark position deviation, generates a refined sequence, and inputs the refined sequence into a downstream task network for generating final prediction. According to the method, the perception robustness is obviously improved.

A Biological Neural Network (BNN) core unit comprising a neural cell culture, an input stimulation unit, an output readout unit may be controlled through its various life cycles to provide data processing functionality. An automation system comprising an environmental and chemical controller unit adapted to operate with the BNN stimulation and readout data interfaces facilitates the monitoring and adaptation of the BNN core unit parameters. Pre-processing and post-processing of the BNN interface signals may further facilitate the training and reinforcement learning by the BNN. Multiple BNN core units may also be assembled together as a stack. The proposed system provides a BNN Operating System as a core component for a wetware server to receive, process and transmit data for different client applications without exposing the BNN core unit components to the client user while requiring significantly less energy than conventional silicon-based hardware and software information processing for high-level cognitive computing tasks.

A Biological Neural Network (BNN) core unit comprising a neural cell culture, an input stimulation unit, an output readout unit may be controlled through its various life cycles to provide data processing functionality. An automation system comprising an environmental and chemical controller unit adapted to operate with the BNN stimulation and readout data interfaces facilitates the monitoring and adaptation of the BNN core unit parameters. Pre-processing and post-processing of the BNN interface signals may further facilitate the training and reinforcement learning by the BNN. Multiple BNN core units may also be assembled together as a stack. The proposed system provides a BNN Operating System as a core component for a wetware server to receive, process and transmit data for different client applications without exposing the BNN core unit components to the client user while requiring significantly less energy than conventional silicon-based hardware and software information processing for high-level cognitive computing tasks.

The invention provides an intention-driven semantic evolution mechanism based on a tensor semantic field and an application system thereof, and relates to the technical field of artificial intelligence, semantic networks and cognitive computing. According to the method, five types of semantic primitives including data, information, knowledge, intelligence and intention are expressed as high-order tensor nodes, a semantic tensor field network is constructed, and dynamic evolution and intention driving of semantics are achieved. The system comprises a multi-scale semantic aggregation mechanism, an intention weight diffusion algorithm, a semantic tensor evolution operator and a white box interpretation interface, supports full-link semantic processing from original data to high-level wisdom to intention constraint, and overcomes the defects in semantic representation and evolution, intention fusion and system interpretability in the prior art. And the generative AI system has stronger intention perception, semantic self-optimization and process transparency capabilities, and is suitable for applications such as a semantic perception large model platform, an AI cognitive map system and an interpretable language generator.

The invention discloses a heterogeneous vehicle road agent collaborative cognitive calculation method based on visual cue, which comprises a polymorphic feature conversion architecture composed of a dimension alignment module, a channel alignment module, a visual cue module and a space attention module. Adjusting and aligning the dimension of the intermediate feature map of the heterogeneous agent through a dimension alignment module; and the one-to-one alignment between each channel and the own vehicle feature map channel is realized through the channel alignment module. The visual prompt module adopts a lightweight feature modification mechanism, combines dual design of general prompt and specific prompt, realizes general capture of global feature distribution, provides basic global semantic guidance, and specifically adjusts model behaviors to optimize cognitive performance. And the refined spatial semantic information of the neighborhood features is mapped to the semantic space of the own vehicle features through a spatial attention module, so that accurate spatial level feature conversion is ensured. According to the method, the training and storage cost can be remarkably reduced while invariance and expansibility are ensured.

In one aspect, the inventions include a system for control of a software defined computer network state system. First, an application plane layer is adapted to receive instructions regarding operation of the state system. Preferably, the application plane layer is coupled to an application plane layer interface. Second, a control plane layer includes an adaptive control unit, such as a cognitive computing unit, an artificial intelligence unit or a machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources. A title transfer network element is provided to transfer digital assets via a blockchain. The system may use domain transformations and difference engines.

In one aspect, the invention comprises a system and method for control of a transaction state system utilizing a distributed ledger. First, the system and method includes an application plane layer adapted to receive instructions regarding operation of the transaction state system. Preferably, the application plane layer is coupled to the application plane layer interface. Second, a control plane layer is provided, the control plane layer including an adaptive control unit, such as a cognitive computing unit, artificial intelligence unit or machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources and to provide output coupled to a decentralized distributed ledger, the data plane layer is coupled to the control plane layer. Optionally, the system and method serve to implement a smart contract on a decentralized distributed ledger.

The invention relates to a neural electrophysiological signal analysis technology in the field of brain cognition calculation, in particular to a cross-individual EEG driving fatigue detection method based on multi-feature comparative learning. The method comprises the steps that PSD features and DE features are obtained based on electroencephalogram signal sample data; extracting a shallow PSD semantic feature, a shallow DE semantic feature, a deep PSD semantic feature and a deep DE semantic feature; obtaining a PSD prediction classification label and a DE prediction classification label based on the deep semantic features; defining a positive sample pair, a negative sample pair and a semi-positive sample pair, and constructing a contrast loss function; splicing the shallow semantic features to obtain a preliminary fusion representation, and obtaining a fusion feature vector based on the preliminary fusion representation, the deep PSD semantic features and the deep DE semantic features; and inputting the fused feature vector into a multi-feature decision maker to obtain a fatigue classification label of each sample. According to the method, the adaptability of the model to individual differences is improved, and the generalization ability of the model and the detection robustness are enhanced.

A computer implemented system to assist a user includes a cognitive computation module which can speak to, listen to and / or see. The system can assist in patient care delivery, driving, providing security, customer support and identity verification. It may be configured in a processor of the computing device which receives text data input after converting speech into text. The received text data is analysed by the module using an artificial intelligence and / or decision rules modules. The modules may create a response of text output which is converted to speech data using text to speech module and sent to the communication module to speak out. It identifies posture of human body, or the face of a human or a user using computer connected camera and also receives input from other sensors or devices connected to the computer and sends input to the cognitive communication module for analysis and response.

A system and method for implementing persistent cognitive computation through geometric representation of thought in a dynamic latent manifold. The system encodes inputs into a curved space characterized by time-evolving metric tensors, compression pressure fields derived from Ricci curvature, and goal potential fields that shape attention flow. Cognition occurs through geodesic traversal of this manifold, with attention following paths that minimize cognitive action while balancing semantic density and goal relevance. A Cognitive Dynamics Engine maintains manifold geometry, computing optimal trajectories and managing thought bundle operations including consolidation, expansion, and higher-order abstraction. During idle periods, autonomous dreaming processes reorganize the manifold through perturbation, recombination, and topological surgery. This architecture enables persistent memory through geometric encoding, where frequently accessed concepts develop high-curvature regions and cognitive shortcuts emerge from usage patterns, transforming artificial intelligence from stateless computation to structured motion through shaped memory space.

The invention discloses a lung cancer high-risk group screening questionnaire cognitive calculation correction method and system. The method comprises the following steps: acquiring questionnaire text data of a subject; constructing a BERT-QA model based on the clinical term knowledge graph, analyzing questionnaire text semantics, and extracting structured risk factor data; mapping the risk factor data to a topological space, and constructing a risk topological representation; calculating a dynamic weight coefficient of each risk factor based on an information entropy principle, and generating a risk assessment value of entropy weight optimization; carrying out calibration in combination with regional pollution map data; according to the method, deep learning, topology and fuzzy logic technologies are fused, intelligent analysis of questionnaire texts and dynamic weight calculation of risk factors are realized, environmental pollution factors are effectively integrated, the screening accuracy and individuation level are improved, and the method is suitable for large-scale popularization and application. The method provides technical support for accurate identification and early intervention of lung cancer high-risk groups, and has important clinical application value.

A system and method for implementing persistent cognitive computation through geometric representation augmented with holonomy-based experiential memory. The system encodes inputs into a curved latent manifold and maintains bounded sets of holonomy descriptors at each location, enabling two-component cognitive states comprising position and experiential context. Cognition occurs through holonomy-sensitive traversal where paths depend jointly on geometric structure and accumulated path-dependent constraints. Holonomy generators are created during traversal from prediction errors and constraint encounters, composed into consolidated descriptors, and undergo lifecycle management including reinforcement, decay, and irreversible export to residual constraint regions. This architecture escapes location-only representations by distinguishing cognitive states that occupy identical semantic positions but arise through different experiential histories. The system supports counterfactual reasoning through holonomy switching at fixed locations and preserves semantic memory as compressed transport deformation rather than stored trajectories, enabling scalable experiential learning where repeated patterns strengthen constraints while capacity remains bounded.

The application relates to the technical field of automatic driving and discloses a VLA-world model fusion automatic driving system and a closed-loop training platform for Robotaxi, which comprises an environment perception coding layer used for generating a space-time aligned environment feature vector; a hybrid cognitive computing core layer integrating a reactive subnetwork, a prudent subnetwork, a cognitive uncertainty evaluator and a counterfactual query interface; wherein the reactive subnetwork is used for generating a future trajectory distribution, the prudent subnetwork is used for simulating a future state through the counterfactual query interface to call the reactive subnetwork when the trajectory distribution entropy value exceeds a threshold value, and a control instruction is generated through thought chain reasoning based on a simulation result, the application realizes a deep closed loop of reactive prediction and prudent reasoning through a counterfactual query mechanism, and solves the problems of cognitive fragmentation and reasoning short-sightedness of a traditional modular system when processing long-tail scenes.

The application discloses a multi-modal false news detection method and device, and relates to the field of multi-modal cognitive computing. The method is used for providing in-depth mining and analysis of news information, realizing semantic enhancement of multi-modal information, and mining and application of news sentiment features. According to the BERT model, the first image caption information is subjected to feature coding to obtain a first caption representation and a first empirical representation. A first text representation is subjected to a self-attention mechanism, a full connection layer and averaging to obtain a sentiment representation of comprehensive analysis of the first text representation by all experts and a sentiment tendency evaluation value of comprehensive analysis of the first text representation by all experts. According to the attention weights of a plurality of different sentiment news processors and the first news processing features, news aggregation features for true or false classification are obtained. According to the news aggregation features for true or false classification and a false news detector, a prediction label of the first news is obtained.