76 results about "Cognitive level" patented technology

The invention provides a service robot cross-modal user intention analysis method and system. The method comprises the following steps: firstly, acquiring and analyzing a voice instruction sequence and a visual flow containing a gesture, fusing a task target object and a gesture pointing direction to generate an intention hypothesis, and then projecting the intention hypothesis to a knowledge graph of an environment with a body; the method comprises the following steps: acquiring an intent hypothesis of a user, querying a candidate object set simultaneously matched with spatial constraints and semantic constraints of the intent hypothesis, when ambiguity exists in the candidate object set, generating an inquiry instruction for eliminating the ambiguity according to a relation path in a knowledge graph of the body environment, and finally analyzing and confirming a final user intent according to the response of the user to the inquiry instruction; according to the technical scheme provided by the invention, the ability of the robot to cope with uncertainties such as unknown reference and environment shielding in a real scene is enhanced, the interactive experience closer to the human cognitive level is realized, and the interactive efficiency and satisfaction of the user in a complex environment are also improved.

The invention discloses a personalized teaching content generation method and system based on a digital portrait, and the method comprises the steps: collecting the multi-dimensional learning data of a student in real time, analyzing the data keyword of the student, constructing the portrait of the student, generating a teaching target based on the portrait of the student, and enabling the teaching target to comprise a teaching theme, exercises related to the theme, and knowledge points related to the theme. Converting the teaching target into an executable instruction of a large language model through a structured prompt engineering technology; the large language model generates initial teaching content according to the teaching instruction; verifying and optimizing the generated initial teaching content to ensure that the generated teaching content conforms to a teaching target and can adapt to the current cognitive level and learning requirements of students; and outputting the verified and optimized teaching content to the students and automatically adapting to the content presentation form according to the learning style preference of the students.

The invention relates to the technical field of artificial intelligence, and particularly provides an intelligent proposition method, system and device for simulating expert propositions and a storage medium, and the method comprises the steps: constructing a structured knowledge network fusing knowledge points, cognitive levels and proposition specifications; establishing a material vector database associated with the knowledge point system; according to the target knowledge point, proposition constraint information is extracted from the knowledge network, related materials are retrieved from a vector library, and the related materials are combined into a generative prompt to be input into a large language model to generate a test question first draft; inputting the test question first draft into at least one large language model serving as a simulation answering agent, evaluating the quality of the test questions by analyzing the answering process and result, and outputting the test questions reaching the standard. According to the method, the proposition efficiency and consistency can be remarkably improved, absolute dependence on expert experience is reduced, and automatic output of high-quality test questions is achieved.

The embodiment of the invention provides an intelligent recommendation method and system based on child digital picture book reading. The method comprises the following steps: constructing a recommendation model based on self-supervised learning and a convolutional neural network CNN; in the self-supervised learning module, feature representation of a comparison mechanism optimization model combined with user cognition is introduced; constructing a dynamically updated user portrait, wherein the user portrait comprises a current cognitive level label of the user; in response to a reading request of a user, self-adaptive fusion is carried out on a user portrait and the reading request, an age-worthiness evaluation factor of the picture book is introduced in the fusion process, and the age-worthiness evaluation factor is determined based on the matching degree of the character difficulty, the picture complexity and the user cognitive level of the picture book. And taking the fused label vector as the input of a trained recommendation model to obtain a recommendation list. According to the embodiment of the invention, the feature representation of the model is dynamically optimized in combination with the cognitive development stage of the user, the reading ability difference of different age groups is accurately adapted, and the recommendation accuracy and adaptability are improved.

The invention relates to a wharf safety intelligent monitoring method, device and equipment based on digital twinning and a medium. The method comprises the following steps: firstly, carrying out space-time alignment fusion on multi-source heterogeneous original data of a wharf site to generate a dynamic digital twinborn scene; extracting and matching entity relationships and events based on the dynamic digital twinborn scene and a pre-constructed wharf operation knowledge graph, and generating a dynamic knowledge situation sub-graph; performing graph neural network coding processing on the dynamic knowledge situation sub-graph to obtain coding features of nodes in the graph; risk and conflict analysis is carried out based on the coding features, and a current risk list and a potential conflict prediction list are generated; and generating security alarm information based on the list. By adopting the method, safety monitoring can be improved from a perception level based on simple rule matching to a cognition level fusing semantic understanding and context association reasoning, so that false alarm and missing alarm are remarkably reduced, and accurate early warning of potential risks in a complex working environment is realized.

The application discloses a robot cognitive development method based on ontology semantics, and comprises the following steps: constructing a robot article identification professional knowledge base based on attribute function and ontology information representation of article definition; information determination based on attribute discrimination and semantic search; and robot cognitive development based on attribute information addition. The application simulates the process of human memory, learning and cognition of articles based on an ontology semantic knowledge base, and through machine learning and sensor attribute information, the robot can actively cognize, learn, expand and accumulate learned knowledge and experience according to information data; the robot can continuously develop its cognitive ability through learning, automatically construct a robot article identification professional knowledge base of unknown articles, and based on a semantic structure of triplets, share knowledge and exchange operation logic between man and machine, so that the cognitive level of the robot is improved and the operation experience of an operator is improved.

The application belongs to the technical field of personalized learning, and discloses a multi-level intelligent cognitive tracking method, system, storable medium and terminal, the method comprising: introducing Bloom cognitive domain education target classification, constructing test question knowledge cognitive tensor TKC, collecting learning resources and answer data of learners, and generating a sequence of learner time sequence answer pairs; introducing a multi-attribute cognitive diagnosis method, combining a deep neural network, and constructing a cognitive level mining model; sorting and encoding the cognitive level mining results of the learners to obtain deep representation features, combining a self-attention mechanism, constructing a multi-level intelligent cognitive tracking model, and further predicting the answer performance of the learners on the test questions. The application is beneficial to accurately and finely modeling the overall knowledge structure and specific level of the learners, thereby promoting personalized learning of the learners and providing a new idea for mining and tracking the cognitive state and level of the learners in an online learning platform.

The application discloses a mental health evaluation method and system based on brain area electric stimulation, relates to the technical field of medical and health information science, and creatively integrates brain area electric stimulation into mental health evaluation. The basic cognitive level and neural plasticity potential of a measured person are objectively quantified through comprehensive scores before and after brain area electric stimulation, so that corresponding suitable mental health evaluation test questions are selected based on the basic cognitive level and neural plasticity potential, and the precision, sensitivity of corresponding mental tests and the mental comfort of the measured person are significantly improved. A decay quantization model of a stimulating current from an electrode to the brain is given, the decay quantization model is used to accurately control the electric quantity, and the maximum beneficial effect is realized under the condition of guaranteeing health and safety. Historical data accumulated in the application process can be effectively used to solve medical problems, such as the influence of brain area electric stimulation on child neural development evaluation, early warning of mental illness and brain function reinforcement.

The invention relates to the field of intelligent course recommendation, and discloses an intelligent course recommendation system and method combined with cognitive level recognition, and the method comprises the steps: constructing a multi-dimensional operation task scene through an interactive game task, and generating an initial cognitive state sequence; mapping the initial cognitive state sequence, and guiding an adaptive feature screening mechanism to highlight key behavior features in a specific emotional state so as to construct a cognitive state map; based on the cognitive state map, capturing short-term cognitive fluctuation and extracting core cognitive indexes in different time periods; fusing the cognitive level evolution trajectory with the historical learning trajectory of the student, the knowledge point mastering condition, the individual preference and the emotional state to generate a personalized cognitive level judgment result which reflects the real ability of the student in the current task and the learning demand of emotion adaptation; and dynamically generating a course recommendation strategy according to a personalized cognitive level judgment result. The method has the advantage of improving the judgment precision of the current cognitive level of the student.

The invention relates to the technical field of education informatization, and discloses a teaching system for middle vocational ideological and political education, which comprises a student emotional state acquisition module, a personalized learning path generation module, a virtual reality practice teaching module, an intelligent interaction guide module, a multi-dimensional teaching effect evaluation module and a teacher decision module, a special cognitive development model of the secondary vocational students is established through a secondary vocational student cognitive feature analysis unit, and the particularities of relatively weak cultural basis, insufficient learning initiative, cognitive feature concrete deviation and the like of the secondary vocational students are fully considered. The learning content adaptive unit can dynamically adjust the depth and presentation mode of the ideological and political education content according to the cognitive level and value tendency of the students, and ensures that the teaching content is matched with the acceptance of the secondary vocational students, thereby improving the adaptability and teaching effect of the teaching content.

The invention discloses an intelligent feature cognition detection device for visual detection. The intelligent feature cognition detection device comprises a multi-modal sensing module, a dynamic stimulation generation unit, an intelligent feature analysis unit and a dynamic cognition evaluation unit, according to the method, eye movement, pupil and electroencephalogram three-mode data are integrated, the sampling rate and precision are better than those of the prior art, environment self-adaptive calibration is combined, data integrity is improved, and the nerve and behavior process of visual feature cognition can be comprehensively reflected. Based on a linkage mechanism of a cognitive state and stimulation parameters, four types of stimulants close to an actual detection scene are generated, real-time adjustment of complexity, duration and interval is supported, the method is adaptive to testees with different cognitive levels, and compared with a fixed stimulation scheme, the relevance between a detection result and actual post ability is improved.

The invention belongs to the technical field of English teaching methods, and particularly relates to an intelligent English teaching method and system and a storage medium, and the method comprises the steps: constructing a multi-dimensional linguistic feature analysis model, and extracting lexical features, syntactic relationship features and semantic deviation features in a text input by a student through a natural language processing technology; dynamic student portraits are established based on the cognitive psychology theory, cognitive level labels are updated according to real-time learning data, and the data comprise grammar error clustering distribution, spoken language fluency indexes and vocabulary association response time; and generating a personalized teaching path, matching teaching materials from the hierarchical resource library according to the cognitive level label, and dynamically adjusting the complexity and presentation form of a teaching strategy. Lexical, syntactic and semantic deviation features are extracted through a natural language processing technology, student error types can be accurately positioned, the problem that traditional error correction only stays on surface modification is avoided, and cognitive tags are updated based on real-time learning data.

The invention relates to the technical field of artificial intelligence education, and discloses a personalized English grammar learning system based on artificial intelligence. Comprising a multi-modal data acquisition module, a deep user portrait construction module, a dynamic association knowledge graph module, a multi-modal intelligent diagnosis module, a meta-cognitive guide type learning path planning module, a personalized content generation module, an emotional real-time feedback module, a meta-cognitive training module, a social cooperation module and a closed-loop optimization module. The multi-modal data acquisition module is used for acquiring multi-dimensional and multi-modal data of a learner, and covering related data such as basic information. According to the personalized English grammar learning system and method based on artificial intelligence, by setting a multi-modal data acquisition module and a deep user portrait construction module, basic information, cognitive features, meta-cognitive levels and emotional preferences of learners are captured in all directions; and constructing a dynamically updated deep user portrait by means of a multi-dimensional data fusion algorithm and a time sequence analysis algorithm.

The invention belongs to the technical field of intelligent education and personalized learning, and provides a personalized learning path recommendation method based on a course knowledge point sequence. The method aims at solving the problems that existing learning path recommendation logic association is insufficient, the personalized matching degree is low, and the optimization efficiency is not high. The method comprises the following steps: constructing a knowledge point sequence model by adopting a topological sorting algorithm on the basis of a curriculum knowledge point pre-modification and post-modification relationship, a difficulty level and dependence intensity, and generating a structured knowledge relationship graph; a learner feature model and a knowledge resource model are established, learner features are described from the dimensions of learning targets, cognitive levels, learning styles and the like, and the matching degree with knowledge points is calculated; and constructing an optimization model taking learning cost minimization, mastering degree maximization and path continuity optimization as targets, performing iterative optimization by using an improved intelligent optimization algorithm, and generating a learning path with logic rationality and personalized adaptability. According to the method, the scientificity and learning efficiency of learning path recommendation can be effectively improved.

The invention discloses a learner cognitive diagnosis method oriented to an intelligent education scene, and belongs to the technical field of cognitive diagnosis and education data analysis. According to the method, concept-level multi-dimensional modeling is carried out on the ability and exercise difficulty of a learner by constructing multi-dimensional representation of concept perception so as to describe mastering characteristics of the learner on different cognitive levels; and meanwhile, distinguishing a pre-correction dependency relationship and a semantic approximation relationship, establishing a relationship-perceived concept dependency model, and deducing a potential exercise-concept association structure according to the relationship-perceived concept dependency model. Further, the annotated exercise-concept incidence matrix and the inference incidence matrix are fused in a unified diagnosis layer, and comprehensive evaluation of the knowledge mastering state of the learner is achieved. According to the method, an end-to-end mode is adopted for optimization, the fineness, knowledge coverage and stability of cognitive diagnosis can be effectively improved, better prediction performance and generalization ability are shown on multiple real education data sets, and the method is suitable for intelligent education application scenes such as learning analysis and personalized teaching.

The application provides a cognitive diagnosis method and system based on causal inference to eliminate problem difficulty bias, relates to the technical field of educational data mining, and specifically includes the following steps: preprocessing the historical record of a student's answer to a problem to obtain a training set composed of a student, a problem, problem difficulty and a score; introducing a problem difficulty variable on the basis of an existing diagnosis model, eliminating bias by using causal inference, constructing a cognitive diagnosis model, and training the cognitive diagnosis model by using the training set; inputting a student to be diagnosed, a problem and problem difficulty into the trained cognitive diagnosis model to obtain a cognitive diagnosis result of the student output by the diagnosis model; the application provides a general cognitive diagnosis framework, creates a cognitive diagnosis model based on an existing diagnosis model, eliminates the adverse effects of problem difficulty bias by using causal inference technology, and performs unbiased training on the model to diagnose the cognitive level of the student, thereby improving the accuracy and efficiency of cognitive diagnosis.

The development process of the elderly psychological health attainment evaluation tool provided by the embodiment of the invention comprises the following steps: carrying out literature review and quality research, and preliminarily combing dimensions and an entry pool of the elderly psychological health attainment evaluation tool; an expert consultation method is adopted, and the dimensionality and the entry pool of the elderly psychological health attainment evaluation tool are improved; writing a first draft of a scale according to the perfected dimensions and entries, and modifying and optimizing the scale to form a first version of elderly mental health attainment evaluation tool; and based on the modified and optimized scale, measuring indexes of the elderly psychological health attainment evaluation tool are detected, and a formal elderly psychological health attainment evaluation tool is formed. According to the method, a COSMIN standard is used as a methodology basis, and a new concept framework of mental health attainment is used as a basis for compiling evaluation tool entries. Through scientific question design and data collection and analysis methods, scientificity and reliability of the evaluation tool are ensured. The entry in the invention adopts an expression mode suitable for the cognitive level of the elderly, so that the elderly can accurately understand the evaluation content.

This invention relates to the field of cognitive health companionship technology, specifically to a multimodal companionship system for patients with cognitive impairment based on AI technology. The invention receives memory materials from a family member's terminal, constructs an interactive virtual scene, configures guiding questions, records the patient's answers to all guiding questions, sets the initial difficulty level of cognitive training based on an assessed memory recall integrity score, and collects performance data after the patient completes cognitive training tasks to determine if the initial difficulty level needs adjustment. This achieves precise matching of cognitive training difficulty to the patient's current cognitive level. Simultaneously, based on the performance data of cognitive training tasks within a subsequently set training period, a cognitive ability report is generated and compared with historical cognitive ability reports in multiple dimensions to assess the patient's cognitive ability improvement. This reflects the dynamic changes and improvement trajectory of the patient's cognitive ability, effectively improving the targeting and effectiveness of cognitive training.

The application discloses a Bloom cognitive hierarchy constraint-based achievement-oriented education diagnosis method and system. The method takes course outline text and student evaluation data as input, and realizes the automatic diagnosis of course goal achievement through three structural modifications in the large language model Transformer architecture: the logarithmic value of the cognitive hierarchy transfer matrix is embedded as a learnable bias item in the attention score calculation path to realize feature coding of cognitive hierarchy perception; the continuous differentiable relaxation technique is used to establish an end-to-end differentiable inference path for the course goal achievement weight; and the Bloom partial order constraint is coded as a training loss, so that the diagnosis result meets the cognitive hierarchy progressive relationship. The three modifications are optimized by a joint loss function, which significantly improves the diagnosis accuracy and promotes the interpretability and scientificity of the large language model diagnosis in the achievement-oriented education scene.

The learning path recommendation method based on neural collaborative filtering and cognitive level diagnosis disclosed in the application comprises: obtaining learner information; modeling a learner portrait according to the learner information to obtain a learner feature vector; constructing a knowledge point feature vector according to a knowledge graph; inputting the learner feature vector and the knowledge point feature vector into a neural collaborative filtering network to obtain a set of explicit and implicit weak knowledge points of the learner for the knowledge points that should be mastered; inputting the set of explicit and implicit weak knowledge points into a cognitive level diagnosis model, sorting, combining and disassembling the knowledge points by the cognitive level diagnosis model, and outputting a final personalized learning path recommendation list. The learning path recommendation method based on neural collaborative filtering and cognitive level diagnosis can better adapt to the cognitive level and learning characteristics of the learner and improve the real-time performance and robustness.

The application discloses a multi-dimensional tensor-based test literacy cognitive level intelligent labeling method and system, in order to solve the problem of multi-dimensional literacy rating performance collapse caused by expert labeling scarcity, loss of mathematical symbol semantics and long tail distribution in the prior art, a three-dimensional tensor space is constructed by fusing course standards; the first data set is used as a few-sample example, and a diagnostic thinking chain is combined to drive a large language model to generate structured pseudo-labels for unlabeled test questions in the second data set; through the incremental pre-training of the mask language model by injecting mathematical symbol prior and combining the cost-sensitive weighting mechanism based on real slot statistics, the lightweight training of the high-dimensional multi-task joint rating network is realized; finally, the active backflow closed loop of low confidence samples is executed through confidence evaluation. The application greatly improves the detection rate and rating accuracy of high-order long tail literacy under very small samples, significantly reduces the labeling cost, and realizes the engineering intelligent evaluation of cross-version mass question banks.

The invention provides a multi-modal large model fine-tuning corpus production method for the field of planning and natural resources, and aims at solving the problems that existing corpus lacks professional semantics, manual annotation is low in efficiency, and no standardized process exists. According to the method, an exclusive VQA corpus is generated through four core modules including construction of an industry exclusive cognitive task and VQA template library, image-text pairing generation, template-driven VQA sample generation and automatic quality control optimization in combination with an industry planning standard and a cognitive hierarchy, namely perception-reasoning-association-application. The method comprises the following steps: firstly, automatically extracting visual elements and policy texts of a planning graph, and matching a professional template to generate candidate Qamp; the method comprises the following steps: A, finally outputting a standardized corpus package through semantic detection and expert re-check optimization; according to the method, professional semantic accurate alignment is realized, the labor cost is greatly reduced, the corpus quality is reliable, the method can be expanded to similar fields, and efficient support is provided for field multi-mode large model fine adjustment and capability evaluation.

The invention discloses an interactive learning method for an educational robot, which aims at overcoming the defects of an existing educational robot in the aspect of constructing comprehensive and interactive learning experience, adopts a multi-modal interaction technology, combines technologies such as voice recognition, text analysis and computer vision, supports various input modes such as voice, text, handwriting and gestures, and improves the learning experience of the educational robot. And the evaluation result is optimized through expression and concentration detection, so that the accuracy and comprehensiveness of evaluation are improved. According to the method, each dimension of answering the question by the user can be evaluated in real time, the question content of the next round is dynamically adjusted according to the answering condition of the user, the learning difficulty is ensured to be matched with the cognitive level of the user, the interactive learning effect is improved, and compared with a traditional education robot adopting a fixed question and scoring standard mode, the learning efficiency is improved. According to the invention, self-adaptive adjustment can be realized, so that the learning process is more targeted and interactive.

The invention discloses an online education intelligent question and answer method and system based on a knowledge graph, and the method comprises the steps: converting a user learning log and historical performance into a dynamic cognitive state feature vector, and introducing the dynamic cognitive state feature vector into sub-graph extraction and reasoning logic of the education knowledge graph as a core constraint. According to the method, the system and the system, by converting a first repair relation of a teaching outline into a graph topology constraint, the system realizes conversion from static knowledge retrieval to self-adaptive cognitive path optimization, generated answers not only fit questions semantically, but also are accurately aligned with the current cognitive level of a user in knowledge difficulty and granularity, so that the user experience is improved, and the user experience is improved. The defect that answers are too difficult or too easy in a traditional scheme is effectively overcome, and the accuracy of knowledge transmission and personalized tutoring efficiency in an online education scene are remarkably improved.

The invention provides a large model agent system and method for shield tunnel structure state analysis, and the large model agent system employs a layered decoupling system architecture. The system sequentially comprises a natural language interaction layer, a large model agent cognition layer, a task planning and tool scheduling layer, a structure state understanding and reasoning layer and a shield tunnel structure state spatio-temporal data and knowledge supporting layer from top to bottom. By means of the layered architecture design, efficient, systematic and intelligent analysis of the shield tunnel structure state can be achieved under the conditions that the number of components is huge and structure indexes are complex, and the structure state cognition level and the decision support capacity of the shield tunnel in the operation and maintenance stage are remarkably improved.

The invention relates to a cognitive diagnosis and reverse thinking training education system based on double AI cooperation, and belongs to the technical field of education technology and artificial intelligence. The system comprises the following steps: acquiring and preprocessing multi-modal data in a learning process of a student, and outputting a multi-dimensional cognitive feature vector; dynamically dividing cognitive hierarchies based on the vector, constructing differentiated cognitive portraits, and generating a personalized training task matrix in combination with a mapping rule base; positioning a cognitive vulnerability and logic error normal form, calling a meta-cognitive question chain library to generate a progressive question sequence, and matching with a scenarized verification task to form an error and omission repairing and knowledge strengthening set; and constructing a group cognition map pre-generality difficulty training module, constructing a dynamic mastery degree evaluation index system, and generating a multi-dimensional capability feedback report. Accurate diagnosis and personalized training adaptation of the cognitive state of the student are achieved, knowledge vulnerabilities are efficiently repaired, the reverse thinking ability is enhanced, and the pertinence and effectiveness of learning training are improved.

The invention discloses an interactive cognitive impairment detection device, which is applied to the technical field of postoperative cognitive impairment detection.The interactive cognitive impairment detection device is provided with a display controller, virtual reality interactive glasses, a tactile feedback device and an interactive cognitive impairment detection system, so that a medical worker can perform interactive cognitive impairment detection work with a patient; visual, listening, touching, sniffing and other feelings can be added, the situation that the cognitive function of the patient is evaluated only through languages, charts, characters and the like is avoided, therefore, comprehensive judgment can be conducted through added visual cognition, touch, hearing and even smell, the cognitive level of the patient can be judged more accurately, the limitation of the cultural level of the patient on the test is avoided as much as possible, and the test efficiency is improved. Moreover, interaction with the patient can be added, so that more accurate evaluation is realized, and the occurrence proportion and degree of the postoperative cognitive impairment of the patient can be evaluated more accurately and scientifically.

The invention relates to the technical field of education management, and discloses a learning cognitive path planning system based on a cognitive map. Comprising a cognitive map construction module for recognizing subject knowledge points, extracting a dependency relationship and constructing a cognitive map; the evaluation portrait construction module is used for constructing a learning evaluation portrait; the difference atlas drawing module is used for identifying a difference atlas in the cognitive atlas; the path calculation and screening module is used for calculating the path value of the feasible path and screening out a learning cognitive path; according to the method, the cognitive dynamic change condition of the learner can be regularly monitored and analyzed, the continuous cognitive updating effect of the learner along the timeline is realized, and the limitation and hysteresis existing when a learning evaluation portrait is constructed by single-dimensional static data are avoided; and the phenomenon that part of associated subject knowledge points are missed and lost due to direct comparison of the real-time cognitive level and the planned cognitive level is avoided, and the planning accuracy of the learning cognitive path is improved.

The invention discloses a large language model adaptive content generation method based on a multilayer knowledge graph and preference perception learning. The method comprises the steps of constructing a hierarchical data set according to questions q and answers in an encyclopedia book, constructing a training data set according to a preset selection instruction p and the hierarchical data set, constructing an encyclopedia knowledge natural language model, training the encyclopedia knowledge natural language model according to the training data set to obtain a trained encyclopedia knowledge natural language model, and obtaining a knowledge point graph G according to the question and answer data set E, and obtaining a reasoning answer according to the knowledge point graph G, the cognitive level of the questioner, the trained encyclopedic knowledge natural language model and the question q'of the questioner. According to the method, the hierarchical data set and the multi-layer knowledge graph with the aligned cognitive levels are constructed, so that the requirements of learners with different cognitive levels are effectively met, and the problem of cognitive dislocation of a current large language model in education application is solved.

The invention discloses a teaching dialogue cognition level dynamic classification method and system based on time entropy, and belongs to the field of artificial intelligence education. In order to solve the problems that an existing method depends on static characteristics and ignores dynamic interaction and class imbalance, a new model with better performance on a known reference data set is provided. The scheme comprises the following steps: calculating teacher-student speaking time entropies to quantify interaction and divide teaching styles; semantic richness features are extracted; and constructing a dynamic weighted heterogeneous integrated model, fusing XGBoost and LightGBM, and dynamically adjusting the weight according to category distribution and minority class performance. Compared with a traditional method, the model achieves more accurate and robust classification performance on a reference data set, especially improves the recognition capability of minority classes, and provides an advanced modeling scheme for an intelligent teaching system. The abstract drawing is Figure 1.