374 results about "Personalized learning" patented technology

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

The invention relates to the technical field of personalized learning. The method comprises the steps that a learning target and a cognitive state vector serve as joint input information to be sent into a large language model, and a knowledge point set and learning gap information corresponding to the learning target are generated; and aligning the knowledge point set with a pre-repair relationship and a dependency relationship in a preset educational knowledge graph to obtain an alignment result, and when a difference value between the updated cognitive state vector and the cognitive state vector exceeds a preset cognitive state change threshold value, determining a learning target based on the updated cognitive state vector and the learning target. And performing semantic analysis and reasoning of the large language model again to obtain a new knowledge point set, determining a new learning resource sequence according to the updated personalized learning path, and pushing the learning resource sequence to the user terminal. The method has the effect of realizing closed-loop optimization of the learning scheme.

The invention relates to the technical field of intelligent education, and discloses a teaching programming system based on large language model assistance and a teaching recommendation method. According to the system, a multi-dimensional ability graph and a knowledge point topology network are constructed, and a large language model is utilized to carry out multiple rounds of intention analysis and semantic reasoning to generate a personalized learning path sequence. The system continuously tracks a learning track, realizes continuous optimization of a teaching strategy by dynamically calibrating strategy parameters, and actively configures special reinforcement resources based on track prediction. According to the system, a breakthrough from static recommendation to dynamic adaptation is realized, the accuracy of learning path planning and the timeliness of teaching intervention are improved through deep semantic understanding and a closed-loop optimization mechanism, so that the programming teaching system can really understand learning requirements and adapt to changes of learning states in real time, and the teaching efficiency is improved. And the knowledge mastering firmness and the learning efficiency are improved.

The invention relates to the technical field of personalized learning, and discloses a personalized learning path recommendation system based on a knowledge graph. The system comprises a user portrait module, a knowledge graph construction module, a path generation module and an effect evaluation module. The user portrait module obtains user learning parameters of the learner; the knowledge graph construction module receives learning domain information, constructs a domain knowledge graph based on user learning parameters, sets knowledge node granularity, and performs association analysis on knowledge units to obtain association weight values of the knowledge nodes; the path generation module dynamically plans a learning path according to the association weight value and adjusts the learning path in real time in learning; and the effect evaluation module monitors the knowledge point mastering degree, the learning progress deviation and the path completion rate of the learner, and performs abnormal early warning. The system can provide learning paths fitting individual differences for learners, and improves the pertinence and flexibility of learning.

The invention provides a personalized learning path planning system and method, and belongs to the technical field of emerging software and emerging technical services, and the system comprises a data acquisition module which is used for collecting learning behavior data of a terminal user, obtaining a learner portrait package based on the learning behavior data, and sending the learner portrait package to a server; the path planning module is used for carrying out node matching on the learning ability feature vector and a pre-constructed knowledge and skill map, outputting a to-be-learned content node set, dividing learning advanced levels and generating learning path description containing to-be-learned nodes and the advanced levels, and the path generation module is used for generating a primary path planning scheme, the scheme feasibility verification module is used for carrying out scheme feasibility verification in combination with the learning advanced level and the path adaptation parameters, and then outputting a final executable path scheme, and the scheme execution module is used for executing the final executable path scheme and controlling learning content pushing and progress adjustment. The problems that in the prior art, personalized learning path planning is not high in precision, not high in adaptability, lack of dynamic optimization and the like are solved.

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

The invention relates to the technical field of intelligent education, in particular to an AI-based nursing teaching scene construction and evaluation system. According to the system, a dynamic case script is generated through historical case data, and a virtual patient physiological model is constructed; creating three teaching scenes of community follow-up visit, emergency rescue and traditional Chinese medicine nursing fusion by using a mixed reality technology; a personalized learning portrait of the student is constructed through a pre-test, and accurate matching between the ability of the student and the teaching content is formed; a double-track system skill training module is built, and AI tutor guidance and teacher guidance are integrated; and an ability evaluation system based on operation data is established, and intelligent pushing of adaptive training content is realized. The problems that in traditional nursing teaching, scene authenticity is insufficient, personalized training is insufficient, and clinical thinking is difficult to cultivate are solved, and the clinical operation skill and decision judgment ability of students are improved.

The invention discloses a dynamic learning path planning method and system based on a learner portrait. The method comprises the steps of collecting learning behavior data, learning result data and background attribute data of a target scholar based on a plurality of data sources, and obtaining knowledge point mastery degree vector representation, learning style preference and cognitive ability level to construct a multi-dimensional dynamic portrait of the target scholar; generating an initial personalized learning path of the target scholar based on a preset learning target and the multi-dimensional dynamic portrait of the target scholar; learning process data of a target scholar for related learning resources is monitored in real time, a learning effect evaluation report is generated according to the learning process data, a multi-dimensional dynamic portrait is adjusted to trigger a path replanning mechanism to generate a brand new personalized learning path, and the brand new personalized learning path is pushed. The learning path and the current state of the learner are kept optimally matched all the time, and personalized static planning is upgraded to dynamic syndrome guidance.

The invention discloses an AI-based computer basic course personalized learning path recommendation method and system, and belongs to the technical field of AI-based data processing. According to the system, behavior data, cognitive data and course interaction data of a learner are acquired through a multi-dimensional data acquisition module, a computer basic course knowledge point association network is established in combination with a dynamic knowledge graph construction module, and a personalized learning path is generated by using an improved deep reinforcement learning algorithm. And the path is dynamically adjusted through the real-time feedback module. The core of the method is that a learner portrait is fused with space-time correlation features of a knowledge graph, a cognitive evaluation model is updated in real time through a Bayesian network, the problems that in a traditional recommendation method, paths are solidified, and the dynamic learning state of an individual is ignored are solved, more accurate personalized learning guidance is achieved, and the learning efficiency and effect of a computer basic course are improved.

The invention relates to the technical field of online education, and discloses an online course learning management method based on a knowledge graph. The method comprises the following steps: acquiring multi-modal learning behavior data of a learner, and extracting a deep learning state vector reflecting knowledge understanding depth, learning input degree and cognitive confusion through semantic fusion; and dynamically calculating and updating the logical relationship strength among the knowledge points in the course knowledge graph by using the vector, so that the knowledge structure can adaptively evolve along with the actual cognitive state of the learning group. And generating a real-time personalized learning path based on the updated knowledge graph and the current state vector of the learner. Meanwhile, according to cognitive confusion features in the state vector, intervention measures such as pushing of remedial resources, adjusting of content sequence or starting of self-adaptive testing are triggered in real time. According to the method, the dynamic optimization of the knowledge graph and the accurate and immediate response of learning intervention are realized, and the adaptability and management efficiency of online learning are improved.

The invention relates to the technical field of intelligent education, in particular to a personalized learning path planning method fusing artificial intelligence and education big data, and the method comprises the steps: collecting multi-modal education data of learning behavior data, cognitive process data and emotional state data from a learning terminal; constructing a learner portrait feature vector based on the preprocessed multi-modal education data; establishing a knowledge graph, and dynamically updating a weight matrix based on group learning data; and taking the learner portrait as a state space, taking a learning path decision as an action space, taking a learning effect as a reward function, generating a personalized learning path by adopting near-end strategy optimization, optimizing a path planning model, and generating a recommendation result. Personalized learning path planning is realized by constructing a multi-modal data fusion framework, a dynamic knowledge graph and an intelligent planning method.

The invention discloses an education knowledge graph quality evaluation method based on confidence evaluation, and relates to the technical field of natural language processing, and the method comprises the steps: S1, obtaining multi-source education data, and achieving the data standardization through a subject term unified dictionary and an education field exclusive relationship type mapping table; s2, constructing a dimension weight adaptive model, and dynamically adjusting weights of accuracy, integrity and consistency in combination with subject types, education stages and application scenes; s3, based on the standardized data and the real-time weight, respectively evaluating the confidence of each dimension through double mechanisms, three sub-dimensions and double detection; s4, fusing to obtain a comprehensive confidence coefficient, and if the comprehensive confidence coefficient does not reach the standard, adjusting parameters according to priorities and returning to re-evaluation; and S5, updating a model coefficient according to teacher feedback and student learning effect data to realize iteration. According to the method, the evaluation suitability and accuracy are improved, and reliable quality guarantee is provided for educational knowledge graph supporting teaching and personalized learning.

The invention provides a family-school interaction classroom note analysis system based on OCR and a large language model. The family-school interaction classroom note analysis system comprises a note scanning module, an OCR recognition module, a note analysis module and a family-school pushing module. Through linkage of all the modules, automatic analysis of classroom notes, efficient cooperation of families and schools and personalized learning closed loop are achieved, and transformation of education from unified teaching to personalized cultivation is promoted.

The invention relates to the technical field of education science and technology recommendation systems oriented to personalized learning path optimization, and particularly discloses an education science and technology recommendation system oriented to personalized learning path optimization. The method aims at solving the problems that an existing recommendation system is difficult to dynamically perceive a cognitive state, knowledge structure semantics and dependency relationships are ignored, and the recommendation precision is low due to data sparsity. The system comprises a multi-source data acquisition and fusion module, a dynamic cognitive state evaluation module, a knowledge graph construction and semantic enhancement module, a path generation and optimization decision module and a self-adaptive execution and feedback adjustment module. Through multi-source data fusion, real-time cognitive state quantification, knowledge graph semantic modeling, multi-target optimization path generation and closed-loop feedback adjustment, accurate recommendation of personalized learning paths is realized, and the continuity, rationality and educational effectiveness of the paths are effectively improved.

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 provides a course dynamic optimization method and system, an electronic device and a storage medium, and relates to the technical field of online education, and the method achieves the quantitative association of a course unit-knowledge fragment through a soft mapping matrix, enables the perplexity to be bidirectionally transmitted between the course unit and the knowledge fragment, and achieves the quantitative traceability. The comprehensive confusion degree is obtained by fusing the confusion degrees of the course unit layer and the knowledge fragment layer, so that the high-confusion course unit can be positioned more accurately and explainably, and fine optimization of the course can be realized; through a course optimization decision with cost constraint, automatic generation of a course optimization scheme with controllable cost is realized; besides, personalized learning path optimization of a course structure layer is realized, and a course optimization strategy continuously acts on subsequent learners, so that dynamic evolution of course contents and personalized learning paths is realized.

The invention provides an intelligent teaching assisting system, a learning path optimization method, equipment and a medium, and the method comprises the steps: collecting the multi-channel learning behavior data of a student during the personalized learning interaction with an intelligent teaching assisting terminal through a multi-modal sensor, and obtaining a sequence dependency relationship between learning content units based on a preset teaching knowledge graph; for each teaching stage, learning behavior features are extracted from the multi-channel learning behavior data, structured feature vectors are constructed based on inherent attributes of learning content units, and joint representation vectors are generated through multi-modal fusion; aggregating the joint characterization vector to generate a teaching stage characterization vector, and evaluating a teaching effect based on the teaching stage characterization vector to obtain a learning state deviation degree; and screening teaching stages with substandard teaching effects by using the learning state deviation degree, and optimizing and adjusting a learning path based on a sequence dependency relationship. By adopting the scheme of the invention, closed-loop dynamic optimization of the intelligent learning path can be realized.

The invention belongs to the technical field of education artificial intelligence and deep learning model optimization, and discloses a thinking tree enhancement-based test question knowledge system intelligent labeling method and system.The thinking tree enhancement-based test question knowledge system intelligent labeling method comprises the steps of collecting question data by introducing a thinking chain enhanced data generation mechanism, and generating a high-quality training sample; and a control mechanism is set, a question generation process is optimized, and the generated question is ensured to accord with teaching specifications in the aspects of knowledge point coverage, grade adaptability, difficulty matching and the like. Through a text encoder based on comparative learning optimization, the model can accurately carry out semantic alignment on questions and knowledge point labels, and label path information is fused through a double-coding mechanism, so that the hierarchical relationship between the labels and the capture capability of semantic dependence are improved. Finally, the constructed multi-label prediction model significantly improves the accuracy and generalization ability of knowledge point labeling, and can provide accurate support for personalized learning recommendation and teaching resource allocation.

The invention discloses an intelligent test paper composition and scoring method and system based on a knowledge graph. The method comprises the following steps: S1, constructing a student knowledge graph state sub-graph; s2, constructing a question bank knowledge graph; s3, on the basis of a node embedding modeling method of an improved DeepWalk algorithm, performing joint training and low-dimensional representation learning on knowledge point nodes by utilizing a continuous bag-of-words model of multi-head context aggregation, and constructing a topic adaptation degree scoring index; s4, outputting an optimal test paper scheme set; s5, collecting a current answer record when the student completes the test paper; and S6, extracting a semantic feature vector and a behavior feature vector, constructing a fusion score feature vector, and generating scores of test paper subjective question answers. According to the invention, accurate test paper composition and multi-dimensional subjective question intelligent scoring for individual knowledge states of students are realized, the method is suitable for personalized learning evaluation and teaching feedback scenes in an education evaluation platform, and the method has the advantages of high personalization, fine feedback and automatic scoring.

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 risk early warning method and device, electronic equipment and a storage medium, and relates to the technical field of risk early warning. According to the risk early warning method and device, personalized learning is carried out based on a medical knowledge base in combination with multi-modal semantic embedding, and a user exclusive causal map is generated to consider individual differences; the model is updated in real time, the health state posterior probability is accurately calculated through a filtering algorithm, and misjudgment is reduced; a target event probability and a high-probability causal path are deduced, a hierarchical intervention instruction and an active disturbance calibration mechanism are matched, a user feedback dynamic optimization map is combined, and medical rationality and personalized adaptation are guaranteed, so that the problems that individual differences are ignored, high false alarms and missing alarms are likely to be generated, and the accuracy and the reliability of medical analysis are poor in existing threshold judgment or statistical correlation analysis can be solved. According to the technical scheme of the invention, the technical problems in the prior art are solved, and the technical effects of reducing the false report and missing report rate of health risks, enhancing the individual suitability, maintaining the medical rationality, and improving the continuous effectiveness of the home health service and the user credibility are achieved.

The invention relates to the technical field of learning education, in particular to an AR-based personalized learning education auxiliary method and system. By collecting and synchronously processing multi-modal behavior data such as eye movement, gestures and head orientation, time sequence characteristics capable of accurately reflecting the learning state of a user are constructed, and then a hidden cognitive state sequence is decoded by using a hidden Markov model and inflection points of the hidden cognitive state sequence are recognized; finally, dynamic and automatic adjustment of learning contents is realized by means of a reinforcement learning model, deep cognitive state changes can be captured from continuous and dynamic user behaviors, accurate teaching intervention is timely performed at'inflection points' of key transition of cognitive states, personalized adaptive learning path planning is realized, and the learning efficiency is improved. The pertinence and effectiveness of learning are obviously improved; the technical problem that an existing AR learning system is difficult to intervene in time at an inflection point where a user cognition state is changed during path planning, so that real personalized learning path planning is realized is solved.

The invention discloses an intelligent adaptive personalized learning method and system based on AIGC and a knowledge graph. The technical problems that an existing online learning system is low in individuation degree, a knowledge system is fragmented, the evaluation dimension is single, the adaptive capacity is weak, and different requirements of teachers and students are not distinguished are solved. The method comprises the following steps: constructing and updating a domain knowledge graph library with weights, establishing a self-adaptive course resource library associated with knowledge entities, respectively generating multi-dimensional portraits of teachers and students, generating differentiated personalized learning / teaching paths based on an A * search algorithm, and realizing self-adaptive content recommendation, continuous self-adaptive evaluation and dynamic adjustment through a mixed recommendation algorithm. A closed-loop intelligent ecology is formed; the corresponding system comprises core modules such as a knowledge graph library, a self-adaptive course resource library and a teacher and student portrait module. According to the method, all-dimensional personalized support of teachers and students, dynamic self-adaptive adjustment and resource generation according to needs are realized, the teaching and learning efficiency is remarkably improved, and the method is suitable for practical subjects and has system self-evolution ability.

The invention discloses a wrong question analysis and personalized recommendation method and device based on a large model and a server, and relates to the technical field of learning assistance and artificial intelligence combination, and the method comprises the steps: obtaining input question-answering information, inputting the processed question-answering information into a preset artificial intelligence model, analyzing the question semantics and the question answering process of the question-answering information, and obtaining a question-answering result; error reasons are positioned, and weak knowledge points are analyzed; based on the error reason of the question answering information, extracting error question core features; on the basis of the extracted wrong question core features, questions which reach a preset association degree with wrong question reasons are retrieved from a preset question bank, and / or three questions related to the wrong question reasons are dynamically generated, the questions which reach the preset association degree with the wrong question reasons are output, and the questions are preferentially matched with the wrong question reasons. The invention provides a method and an intelligent system which are combined with a large model technology, can deeply analyze error causes, supports multi-modal data and can dynamically generate questions, and can realize precise and personalized learning assistance.

The invention discloses a personalized learning scheme recommendation method and system based on artificial intelligence, and relates to the technical field of intelligent recommendation, and the method comprises the steps: collecting a learning behavior track and a knowledge mastering progress, carrying out the normalization processing, and generating structural feature input; performing multi-dimensional feature mapping and weight training on the structured feature input by using a reinforcement learning model to generate a learning preference vector; matching the learning preference vector with the weighted feature interaction matrix, calculating an adaptive score, and generating a candidate resource set; and performing iterative fusion on the candidate resource set and the real-time interaction feedback vector to generate a feedback fusion adaptive score matrix, updating adaptive scores by adopting a dynamic adjustment method, forming an optimized resource sequence, converting the optimized resource sequence into a task execution path, and generating personalized learning scheme recommendation. According to the invention, the accuracy of learning resource matching and the timeliness of personalized recommendation are improved.

The invention relates to the technical field of data processing, in particular to a personalized learning path planning method and system. Comprising the following steps: acquiring a behavior data sequence arranged according to a time sequence, and extracting a repetitive mode in a learning behavior and a difference point deviating from a standard learning process to obtain a behavior difference index; dynamically adjusting an association structure between nodes in the knowledge graph, identifying key knowledge nodes, determining a missing concept set, and inserting missing concepts into a current learning path to generate a supplementary path draft; by simulating a plurality of alternative learning paths and calculating coherence scores of the alternative learning paths, screening and sorting to obtain a plurality of personalized learning path options; and integrating the behavior data newly generated by the learner through a feedback module, and outputting a final dynamic learning path. According to the method, the problems of insufficient dynamic adaptability and difficulty in effectively utilizing the behavior data of the learner in the existing learning path planning are solved, and dynamic and personalized learning path planning based on the behavior difference of the learner is realized.

The invention relates to a self-adaptive evaluation and precise learning path generation system based on a knowledge graph. The method comprises the following steps: constructing a standard knowledge graph and an error region knowledge graph; performing misunderstanding mode matching based on the learning interaction data of the students, updating the misunderstanding knowledge graph of the students, and generating a personal misunderstanding knowledge graph of the students; setting a graph conversion operation from the student personal misunderstanding knowledge graph to a standard knowledge graph, and determining a target graph conversion operation sequence through a search algorithm by taking the student personal misunderstanding knowledge graph as an initial state and taking a sub-graph corresponding to a learning target in the standard knowledge graph as a target state; the target image conversion operation sequence is a personalized learning path. The problems that cognitive errors are difficult to eradicate and learning paths are poor in adaptability can be effectively solved, and meanwhile by means of scientific cost evaluation and an intelligent search algorithm, it is ensured that the generated learning paths conform to student individual cognitive characteristics and have high efficiency and feasibility.

The invention relates to the technical field of computers, in particular to an intelligent error data management method and device, and the method comprises the steps: obtaining historical error data and historical answering behavior data of a student, and constructing a subject knowledge graph and a student cognitive ability portrait; the method comprises the following steps of: performing semantic coding on a wrong question text through a pre-trained Transform model; based on the encoded vector representation, associating the error questions to corresponding knowledge point nodes in the subject knowledge graph, and identifying error types corresponding to the error questions; according to the knowledge point node attributes of the subject knowledge graph associated with the error questions and the identified error types, the cognitive competence portrait of the student is updated, and the mastery degree weight of the associated knowledge point nodes in the subject knowledge graph is dynamically updated; and based on the updated student cognitive competence portrait and subject knowledge graph, optimizing a cognitive state through LEMMA type reflection training, and generating a personalized learning path. According to the invention, accurate and personalized learning guidance can be provided for students.

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 relates to the technical field of intelligent education planning, and discloses a dynamic course planning method based on a knowledge graph. The method comprises the following steps: acquiring real-time learning data including knowledge point mastering degree, learning behavior and progress; and performing association analysis on the data by using the subject knowledge graph to generate a learning state representation vector reflecting knowledge point association strength and path dependency degree. And inputting the vector into a course planning model for calculation to obtain an adjustment scheme containing the knowledge points to be strengthened, the path rearrangement sequence and the resource index. According to the scheme, the node weight of the corresponding learner in the knowledge graph is updated, and a dynamically evolved personalized knowledge graph is formed. And generating a next-stage learning task instruction based on the graph, wherein a logic mapping relationship between the to-be-learned knowledge point sequence and a preset learning target is clearly revealed. According to the method, structured traceability and diagnosis of learning weak links are realized, and an interpretable personalized learning path with a clear logic basis can be provided.