233 results about "Knowledge state" patented technology

The invention discloses a concept recommendation method based on large language model structure learning and knowledge representation, and belongs to the field of concept recommendation. The method comprises the following steps: firstly, acquiring learning data and a concept knowledge graph of a student account in a learning process, and preprocessing the learning data and the concept knowledge graph; performing text coding on an enhanced text generated by the large language model by using a pre-training language model to obtain a final hidden vector of each concept, further constructing an enhanced concept knowledge graph, and pre-training a GraphGPS concept encoder based on the enhanced concept knowledge graph and a double-view cross graph contrast learning method; synthesizing education data by a TabDDPM model to pre-train a deep knowledge tracking model; and splicing the concept representation of question embedding, answer embedding and structure perception and the student knowledge state representation to form a final representation, further obtaining a coding sequence, pre-training a sequence coding model and finely adjusting a concept recommendation matrix, and finally forming a concept recommendation model for performing concept recommendation.

The invention relates to the technical field of educational resource recommendation, and discloses an educational resource intelligent recommendation method and system based on big data driving, and the system comprises a data fusion processing module, a portrait modeling module, a resource feature engine module, an intelligent recommendation core module and a closed-loop feedback module. By fusing knowledge state, cognitive ability and interest preference three-dimensional portraits, capturing user learning ability evolution in real time, dynamically updating knowledge mastery by adopting a knowledge tracking model, and perceiving interest migration in combination with an attention mechanism, the problem of learning cold start in a new field is solved, recommendation coverage range and accuracy are improved, and user experience is improved. The cognitive load sensitive ant colony optimization algorithm is designed, the learning path continuity is guaranteed through a heuristic function, the learning path structure reasonability is optimized, the cognitive burden of a user is reduced, a personalized knowledge attenuation model is constructed by fusing an Ebbinghaus forgetting curve, the knowledge long-term retention rate is increased, and the review resource release accuracy is enhanced.

The invention discloses an intelligent question bank retrieval and recommendation system based on an artificial intelligence knowledge graph, and relates to the technical field of artificial intelligence education. Comprising a knowledge graph construction module which is used for processing original education data and constructing a neighborhood knowledge graph comprising a hard preposition relation and a soft incidence relation; the user knowledge state graph construction module is used for constructing a user personal knowledge state graph isomorphic to the domain knowledge graph, and dynamically calculating a mastery index of each knowledge node through a deep knowledge tracking model based on user historical answer data; according to the method, by constructing the domain knowledge graph containing the hard preposition relation and the soft incidence relation, discrete knowledge points are organized into the structured network conforming to the cognitive law, so that the system can understand and follow the internal logic between knowledge, and a learning path which is clear in organization, efficient and coherent is generated.

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

Embodiments are directed toward a blockchain system including a global state and an assemblage of blocks, each block representing a collection of state transformation records, each state transformation record describing a state transformation performed on the global state, where preceding blocks referenced by any given block contain state transformation records describing state transformations performed on the global state prior to the evaluation of the given block, and where at least one state transformation record is a zero-knowledge transformation record encoding a zero-knowledge state transformation description, which zero-knowledge transformation record comprises at least the following elements: paths identifying the locations of the elements of a discrete data subset, a revised data subset, and a transition proof implemented as a non-interactive zero-knowledge proof, which transition proof proves that the transition from the discrete data subset to the revised data subset follows the established rules of the blockchain system.

The invention relates to a personalized scientific education system based on AI agent driving, and belongs to the technical field of informatics. The education system runs a main agent, a user twinborn agent, a learning planning platform and a graph structure database. The main agent is used for collecting knowledge states and learning behaviors of users, constructing a user model, and training one or more user twin agents based on the user model. And the user twinborn agent is used for simulating learning behaviors and results of the user under different learning paths. And the learning planning platform combines the user model and the knowledge graph stored in the graph structure database to generate a plurality of candidate learning paths for the twin intelligent agent of the user to perform analogue simulation. And the main agent selects an optimal path according to a simulation result and implements the optimal path to the personalized teaching process of the user. According to the technical scheme, high-adaptability learning path recommendation and dynamic adjustment are realized, and the intelligent level of individualized teaching is improved.

The invention relates to the technical field of hydrological survey skill training, in particular to a construction method and system of a hydrological survey knowledge model based on a large model. The method comprises the following steps: acquiring multi-source hydrological data, and constructing a hydrological knowledge graph; generating a confusion sample corresponding to the hydrological knowledge graph, and performing adversarial training on the confusion sample; according to the confrontation training result and historical test questions in the multi-source hydrological data, obtaining bidirectional mapping test questions; inputting the bidirectional mapping test questions into a question setting interface of a hydrological survey learning platform; collecting answer data and student physiological data corresponding to the bidirectional mapping test questions in real time, and updating a student knowledge state according to the answer data and the student physiological data; constructing a hydrological survey knowledge model based on the updated student knowledge state; and generating a student recommendation learning scheme by using the hydrological survey knowledge model. According to the invention, the intellectualization and practicability of hydrological survey education can be improved.

The invention relates to a knowledge graph-based teaching resource database construction method and system, and belongs to the technical field of data resource processing, and the method comprises the following steps: S1, obtaining and preprocessing a multi-source heterogeneous teaching resource: carrying out the natural language processing of an unstructured text to recognize the association of an entity and a key concept, analyzing the user behavior log to extract hidden knowledge point association; s2, defining a multi-dimensional teaching knowledge graph mode layer: constructing a multi-dimensional graph mode layer; s3, collaborative extraction and fusion of graph entities and relationships: collaborative extraction of core teaching entities and specific relationship types from the preprocessed teaching resources to construct a knowledge graph; s4, adaptive graph updating and teaching path generation: taking the constructed knowledge graph as an underlying database, and responding to knowledge state query of a user; the method has the beneficial effects that massive and heterogeneous teaching resources and the knowledge graph are effectively fused.

The invention discloses a dynamic course planning method based on an adaptive learning algorithm. The method comprises the following steps: S1, constructing a multi-dimensional data model of a learner knowledge space; s2, forming a knowledge graph represented by fractal characteristics; s3, identifying weak areas and potential knowledge blind spots in the knowledge space of the learner; s4, recursively decomposing the knowledge graph by using a fractal recursive algorithm according to a real-time knowledge state analysis result of the learner in combination with a learning target; s5, optimizing recommendation content and performing dynamic adjustment according to knowledge mastering conditions, weak knowledge points and learning behavior preferences of the learners; and S6, according to the learning resources recommended in the step S5, by monitoring the performance data of the learner in the learning process in real time and combining a performance data analysis result, dynamically adjusting a learning path and resource recommendation content, optimizing learning suggestions and updating the knowledge state of the learner in real time until course planning is finished. According to the method, the personalized learning requirement can be better met, and the learning effect and the cross-scene adaptability of the system are improved.

The invention discloses an Agent-based education evaluation method and system, and relates to the technical field of artificial intelligence and education evaluation, and the method comprises the steps: obtaining historical evaluation data and knowledge graph labeling data of students, constructing a student knowledge state matrix, calculating a mastering probability value and a forgetting attenuation coefficient of each knowledge point, and generating an evaluation question recommendation sequence; inputting the question text and the student answering text into a multi-Agent collaborative evaluation model to generate a multi-dimensional scoring result; dynamically updating the knowledge point mastering probability value of the student in the continuous answering process, calculating the mastering degree change rate of each knowledge point node, constructing an adaptive difficulty adjustment function, and generating a question difficulty parameter and a knowledge point coverage range parameter of the next round of evaluation; and taking the question difficulty parameter and the knowledge point coverage range parameter as constraint conditions, screening a candidate question set meeting the conditions from a question bank, and generating a self-adaptive evaluation path and a capability diagnosis report. The invention also discloses a method.

The invention relates to a continuous updating method and system for a visual large model, and belongs to the technical field of artificial intelligence. Comprising the following steps: extracting visual data features by using a Mamba model to obtain a first feature representation, and embedding position information to obtain a second feature representation; constructing an image block sequence according to the second feature representation, and combining the first feature representation and the second feature representation to construct a bidirectional state space model and a knowledge state; constructing a Prompt template and introducing the Prompt template into a visual Mangbar model to obtain first enhanced feature representation; inputting the image block sequence into the bidirectional state space model, simulating the time sequence change and dynamic characteristics of the visual data, and combining the first enhanced feature representation to obtain a second enhanced feature representation; inputting the feature representation into a multi-modal pre-training model for fusion to obtain fusion data; and through an online learning mechanism, processing new visual data in real time, and updating model parameters. According to the method, the problem of model performance reduction caused by disastrous forgetting in a continuous learning task is solved.

The invention discloses a deep knowledge tracking method based on a graph neural network, and belongs to the field of wisdom education. The method specifically comprises the following steps: firstly, cleaning a real data set, and storing a binary relationship between a problem and a skill in each piece of interactive data into a dictionary; then, all problems of the same student form a sequence according to incremental time steps, corresponding exercise sequence diagrams and interaction sequence diagrams are generated according to a binary relation, then, a corresponding graph neural network is constructed, and representation of each graph in the exercise sequence diagrams and the interaction sequence diagrams is obtained respectively; splicing the exercise map representation of the t + 1 time step and the interaction map representation of the t time step, and predicting a learning result of the t + 1 time step of the student through a full-connection neural network; and finally, the graph neural network is embedded into an existing Transform model, and parameters are updated through back propagation. According to the method, the dynamic graph representation of the problem and the interaction sequence is realized, the knowledge state is updated by utilizing an attention mechanism, and the performance and the interpretability of the model are improved.

The invention relates to the technical field of artificial intelligence education, and discloses a personalized online education system and method based on artificial intelligence, and the system comprises a data collection and perception module which is used for collecting the dynamic behavior data and multi-mode perception data of a learner; the knowledge processing and constructing module is used for processing teaching contents to construct a structured knowledge graph; the user modeling module is used for constructing a dynamic learner portrait comprising a knowledge state model and a cognitive-emotional state model for the learner based on the collected data and the constructed knowledge graph; and the intelligent decision and intervention module is used for generating and executing personalized learning intervention based on the dynamic learner portrait. According to the method, by fusing multi-mode cognitive state perception, reinforcement learning intervention decision and a knowledge graph dynamic evolution mechanism, accurate and timely personalized intervention for learners and intelligent guidance of optimal learning strategies are realized.

Embodiments are directed toward a blockchain system including a global state and an assemblage of blocks, each block representing a collection of state transformation records, each state transformation record describing a state transformation performed on the global state, where preceding blocks referenced by any given block contain state transformation records describing state transformations performed on the global state prior to the evaluation of the given block, and where at least one state transformation record is a zero-knowledge transformation record encoding a zero-knowledge state transformation description, which zero-knowledge transformation record comprises at least the following elements: paths identifying the locations of the elements of a discrete data subset, a revised data subset, and a transition proof implemented as a non-interactive zero-knowledge proof, which transition proof proves that the transition from the discrete data subset to the revised data subset follows the established rules of the blockchain system.

The invention discloses a time enhanced knowledge tracking method based on dual-channel deentanglement, and belongs to the technical field of education data mining and cognitive modeling. According to the technical scheme, the method comprises the steps that time dynamic features and behavior reaction features in a learning interaction sequence are extracted and coded through a time domain encoder and a behavior domain encoder respectively; separating long-term trends and short-term fluctuations in the input features by using a multi-scale decoupling layer based on causal convolution; a time perception dual-channel attention module is adopted to independently decouple time and behavior characteristics after decoupling, and a nonlinear attenuation item based on a real interval is introduced to simulate memory forgetting; and finally, integrating dual-channel information through a gating fusion mechanism and predicting future answering performance of the learner. According to the method, optimization conflicts are effectively relieved, the robustness to a complex learning mode is enhanced, and knowledge state modeling which better accords with a cognitive law is realized.

The invention discloses a personalized knowledge tracking method and system based on a graph neural network and question difficulty, and relates to the technical field of wisdom education, and the method comprises the steps: obtaining a data set of a field to be subjected to knowledge tracking; modeling is carried out on the difficulty of problems and knowledge concepts in the data set, and difficulty information is extracted; taking the difficulty as a node in the meta-path, and fusing the difficulty into a heterogeneous graph neural network model; constructing a difficulty-adaptive knowledge tracking model based on the fused heterogeneous graph neural network model; performing knowledge state updating on the knowledge tracking model; and final knowledge tracking is realized. According to the method, the heterogeneous graph is introduced, the difficulty feature of practice is fully utilized, the limitation on feature mining is overcome, and the accuracy of knowledge tracking is improved. By considering the perception of the individual students on the exercise difficulty and the learning income obtained from the exercises with different difficulties, more refined initial knowledge state modeling is realized, and the learning progress of the students can be reflected more accurately.

The invention provides a learning process-oriented ternary second-order coupling dynamic cognitive diagnosis method, which comprises the following steps of: dividing learning units in a learning process into'knowledge, time sequence and behavior 'triples; knowledge-time sequence and behavior-time sequence first-order coupling analysis knowledge state and behavior state changes are obtained through a neural network model and a behavior feature extraction technology, meanwhile, a guess gate, a proficiency gate and a learning gate are set by adopting a GLU gating technology, complex modeling of cognitive state changes of learners in the learning process is achieved, and the learning efficiency is improved. Obtaining a potential ability state of the learner; finally, through multi-task prediction of behavior characteristics and reaction results, diagnosis of the cognitive state of the learner and prediction of future performance of the learner are achieved. According to the invention, the learning condition of the learner can be scientifically and comprehensively predicted, and the purpose of assisting the teacher in precise teaching is achieved.

The invention provides a hierarchical attention cognition diagnosis method based on knowledge association and abnormal behavior mixing, which comprises the following steps of: aggregating heterogeneous test question attributes such as test question texts, concepts and difficulty by adopting a graph neural network technology, and updating neighbor nodes to obtain knowledge association; a short-term attention mechanism based on working memory capacity is adopted to aggregate preorder knowledge information associated in a time sequence, and first-order coupling of'knowledge-time sequence 'is achieved; key features reflecting student guessing and carelessness are extracted from click stream features, guessing and carelessness functions are set to calculate guessing indexes and carelessness indexes, and first-order coupling of'behavior-time sequence 'is achieved; based on the knowledge state and the abnormal behavior index, setting a guess gate and a carelessness gate, correcting the cognitive state of the student, obtaining the comprehensive ability state of the student, and realizing second-order coupling of'knowledge-time sequence-behavior '; and finally, adopting a multi-task learning method, and introducing an abnormal index prediction task so as to correct the answer performance prediction of the learner in the future.

The invention discloses a knowledge graph teaching path dynamic recommendation method and system based on reinforcement learning. The method comprises the following steps: constructing a cognitive adaptive dynamic knowledge graph; obtaining an individual multi-dimensional state vector of a student to be recommended; obtaining a trained student state perception model and a trained teaching path reinforcement learning model; inputting the individual multi-dimensional state vector of the to-be-recommended student into a trained student state perception model to obtain cognitive state information of the to-be-recommended student; and calling the trained teaching path reinforcement learning model by taking the cognitive adaptive dynamic knowledge graph as an environment and combining cognitive state information of the to-be-recommended student, so as to obtain a current personalized teaching path recommendation result of the student individual. The teaching content sequence can be dynamically adjusted according to the knowledge state and the learning behavior of the student, and the teaching integrating degree is improved; the teaching path is continuously optimized through learning, and different types of students can be adapted.

The invention relates to the technical field of computers, and discloses a fragmentation block chain federal learning method based on a large language model multi-agent, and the method comprises the steps: S1, initializing a client; s2, dynamic fragmentation scheduling and distribution; s3, generating and uploading local knowledge; s4, intelligent agent collaborative routing and knowledge acquisition; s5, knowledge fusion and model updating; and S6, repeatedly executing the steps S3 to S5 until the model converges or reaches a preset number of iterations. According to the invention, under a decentralized and fragmented federated learning architecture, the complex reasoning ability of a large language model and the autonomous cooperation mechanism of three multi-agent systems, namely a fragmented scheduling agent, a fragmented knowledge state agent and a global knowledge routing agent, are deeply fused; according to the mechanism, dynamic optimization of a bottom layer fragment structure and intelligent routing of high-value knowledge are achieved in an intelligent mode, and therefore the overall efficiency and model performance of a system under the condition of heterogeneous data and heterogeneous equipment are remarkably improved.

The invention belongs to the technical field of knowledge tracking, and discloses a knowledge tracking method and system supporting a dynamic context, and the method comprises the steps: constructing a knowledge tracking model supporting the length of the dynamic context based on a question set of a student; and based on the knowledge tracking model supporting the dynamic context length, obtaining a final student answering prediction result. Through a question encoder and an answer encoder, a historical answer sequence of a student is mapped into high-dimensional feature embedding, and a question level perception vector and joint question-answer interaction representation are generated. The attention score matrix is dynamically adjusted based on the directional penalty function matrix, and the adaptability of the model to the dynamic change of the context length is enhanced. A multi-head self-attention mechanism and zoom dot product attention are utilized to extract knowledge states of time sensitivity and context dependence from student interaction data.

The invention discloses an enterprise training evaluation system and method fusing RAG and an intelligent agent, and belongs to the technical field of training evaluation of artificial intelligence. The system comprises a computing node, a storage unit, a network interaction unit, a document vectorization processing module, a dynamic question setting module, a real-time semantic scoring module, a training effect visualization module and a learning recommendation module. The method comprises the steps of document preprocessing, dynamic question setting, real-time scoring, effect visualization and learning recommendation. Through deep fusion of RAG retrieval and agent decision, personalized question setting, multi-dimensional instant scoring, long-term knowledge state tracking and early warning and personalized learning recommendation based on student knowledge states are realized, and an evaluation-feedback-optimization closed loop is formed. According to the method, the question setting pertinence, the scoring accuracy (up to 92.3%) and the training efficiency (compressing the training time by 40%) are remarkably improved, and the method is suitable for various enterprise training scenes.

The invention relates to the technical field of knowledge tracking, in particular to a knowledge tracking method and system based on multi-modal auxiliary information extraction, and the method comprises the steps: obtaining learning interaction data, test question data containing graphic and text information of test questions, and a step-by-step solving process corresponding to each test question, and carrying out the auxiliary feature extraction to obtain an auxiliary information set; constructing a knowledge tracking model embedded based on an auxiliary information set, obtaining question embedding of test questions from learning interaction data of students, and embedding of answer activities covering knowledge concepts and answers of the students; establishing an attention network model, and based on question embedding and answering activity embedding, determining the degree of mastering the question and the knowledge state of the student; and performing data enhancement on the learning interaction data, enhancing representation consistency of knowledge states before and after by utilizing comparison loss constraint data, and predicting subsequent answering performance based on the current knowledge state of the student. Through multi-modal information extraction and an attention network model, the knowledge state of the student is accurately evaluated, and the answering performance is predicted.

The invention relates to the technical field of digital education, in particular to a digital teaching material intelligent generation system based on digital education, which comprises an education resource acquisition module, a knowledge graph construction module, a teaching material content generation module, a knowledge tracking module and a teaching material presentation module, according to the method, a graph embedding method based on a hyperbolic space graph convolutional network and a hierarchical clustering algorithm with improved robustness are introduced, low-dimensional embedding and multilayer structure optimization are performed on an initial knowledge graph, and an educational knowledge graph with a semantic hierarchical structure is constructed; a graph structured learning behavior modeling method is introduced, a student-knowledge point heterogeneous graph structure is constructed, a heterogeneous graph convolutional network is adopted to model interaction behaviors of students and knowledge points, a knowledge state graph of each student is obtained, and the accuracy of individual knowledge mastery degree modeling is improved.

The invention belongs to the technical field of knowledge tracking, and particularly discloses a knowledge tracking method based on various learning behaviors. The knowledge tracking method based on various learning behaviors comprises three parts of data preprocessing and vector representation coding, a multi-head self-attention mechanism fusing a forgetting behavior and a transformation relation between the behaviors, and model training and prediction. According to the invention, on the basis of various learning behavior data, a knowledge tracking problem is modeled from a whole-process learning scene of a learner, so that a conversion relation of different types of learning behaviors and a personalized learning mode of the learner can be modeled by a built knowledge tracking model of various learning behaviors; and meanwhile, a learner forgetting behavior based on learning time and repetition frequency modeling is introduced, so that the knowledge state change condition in the whole learning process of the learner is better modeled, the knowledge state information of the learner is effectively acquired from various learning behaviors, and a knowledge tracking task is completed.

The invention discloses a university education personalized learning path generation method and system based on artificial intelligence, and relates to the technical field of personalized learning path generation, and the method comprises the steps: collecting the multi-modal data of students, obtaining an input vector, and constructing a pre-constructed knowledge graph; performing knowledge state modeling through TransformerEncoder on the basis of the input vector, performing knowledge state updating through graph attention based on a pre-constructed knowledge graph to generate a knowledge state containing a knowledge point relationship, and predicting a mastering probability of each knowledge point; using CNN to extract behavior characteristics of students, generating a cognitive load state through Transform, and finally outputting a cognitive load value; on the basis of the knowledge state and the cognitive load value, constructing double-target learning path optimization, and searching and solving through a Monte Carlo tree to generate a next personalized learning task; the learning effect and the learning experience are remarkably improved.

The invention discloses a personalized knowledge tracking method based on a cognitive dynamic graph and multi-expert mixing, and belongs to the technical field of intelligent education, and the method comprises the following steps: constructing a cognitive dynamic structure graph based on learning interaction data of students, the cognitive dynamic structure graph comprising a cognitive dependence knowledge point graph and a question-knowledge point association graph; information transmission is carried out on the cognitive dynamic structure diagram based on a graph convolutional network, and cognitive enhanced interaction feature representation is obtained; constructing a heterogeneous expert pool and a self-adaptive routing mechanism to differentiate cognition of the students based on the interaction feature representation of cognition enhancement, and generating a comprehensive learning state evaluation result; and on the basis of the comprehensive learning state evaluation result, predicting the performance of the student on the next question, and realizing personalized knowledge tracking of the student. According to the invention, through dynamic knowledge dependence modeling and a multi-expert mixing mechanism, accurate tracking of student knowledge states and efficient planning of personalized learning paths are realized.

The invention relates to the field of intelligent speech synthesis and emotion calculation, and discloses a digital human tour guide speech generation method, system and device and a storage medium, the digital human tour guide speech generation method comprises the following steps: S1, constructing a user mental model comprising an initial knowledge state of a user for a knowledge graph; s2, on the basis of the model, predicting and evaluating candidate narrative paths, planning an optimal path and determining an expected mental state; s3, multi-modal explanation content is generated and broadcasted according to the optimal path; s4, collecting real-time feedback of the user to obtain a real mental state; and S5, comparing the real state with the expected state, calculating a prediction deviation, and dynamically calibrating the user mental model for subsequent planning according to the prediction deviation. According to the method, prospective path planning is carried out by constructing the mental model, closed-loop calibration and robustness evaluation are combined, and personalized explanation which is accurate, stable and free of lag adjustment is achieved.

The invention provides a learning resource recommendation method based on knowledge tracking and retrieval enhancement generation, and belongs to the technical field of education. The method comprises the steps of obtaining data of a user learning platform and / or learning content uploaded by a user, and constructing a background knowledge base related to a current learning task of the user; acquiring learning interaction behavior data of the user, constructing a knowledge state tracking model, and forming a current knowledge mastering state of the user; wherein the learning interaction behavior data of the user comprises knowledge points, exercises, videos and texts; and outputting personalized learning resource recommendation and question and answer response by utilizing a retrieval enhancement generation model in combination with the background knowledge base and the knowledge mastering state. Therefore, dynamic, personalized and knowledge-accurate learning content recommendation can be realized.

The invention discloses a concept recommendation method and system under knowledge graph modal deficiency based on a large model, and belongs to the field of knowledge graph recommendation. According to the method, firstly, a large model is fully utilized to optimize construction of the knowledge graph, then, a richer knowledge graph is constructed through a feature complementation technology utilizing channel confidence to ensure integrity of concept features, and finally, in combination with an online learner model, knowledge states of learners are captured in real time, so that the knowledge states of the learners are captured in real time. Therefore, personalized recommendation of knowledge concepts in the online learning process is realized. According to the method, not only are the integrity and the dynamism of the knowledge graph improved, but also an effective solution is provided for the knowledge recommendation problem in online education.