908 results about "Personalized learning" patented technology

The invention discloses a personalized learning recommendation method based on a personalized knowledge graph. The method comprises the steps that the knowledge graph is constructed, and a learner fills in learning style self-description and learning target expected content; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of the students are analyzed to determine the personal basic conditions of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt the personalized learning features to the learning knowledge element sequence, and a personalized learning push path recommendation mode is constructed to assist the students in improving the learning effect.

The invention discloses an online teaching interaction method based on a multi-modal knowledge graph, a medium and equipment, and the method comprises the steps: firstly collecting original teaching information, and carrying out the cross-modal semantic alignment processing, and obtaining structured teaching information; then constructing a multi-modal knowledge graph containing text concept entities, video key frame feature vectors and voice text transcription contents, and forming a concept-visual feature association edge, a concept-voice segment association edge and a cross-modal similarity association edge; converting the user interaction behavior data into a knowledge graph query vector, extracting a three-dimensional teaching situation sub-graph from the multi-modal knowledge graph, and generating progressive or comparative teaching content according to a user operation type; and finally, dynamically adjusting a teaching strategy by constructing a cognitive state tracking matrix, and generating a personalized learning navigation map. The problems of knowledge fragmentation and insufficient interaction intellectualization in virtual simulation teaching are solved, and the teaching effect of ideological and political education is improved.

Provided is a method of generating a personalized learning path based on multi-course knowledge graph merging and a system thereof, in particular to the technical field of personalized learning path generation. The method includes: constructing a personalized knowledge base to obtain a personalized knowledge graph of all courses that students need to learn in the current academic year; using an entity alignment technology to realize the personalized knowledge graph merging of all courses, and automatically generating a personalized knowledge graph of multi-course merging; generating a subsequent learning path according to a current learning progress of users and the personalized knowledge graph of multi-course merging; and evaluating the generated learning path in terms of link prediction accuracy.

The invention discloses a personalized learning path recommendation system based on artificial intelligence, and relates to the technical field of path recommendation, firstly, the system collects multi-dimensional feature data of a learner, and constructs a personalized feature vector; secondly, in combination with knowledge graph modeling and graph neural network technologies, deeply mining explicit and implicit knowledge point association; then, predicting an optimal learning path by using a sequence recommendation model, and ensuring reasonable sorting of knowledge points; in the learning process, the system combines real-time interaction data, dynamically adjusts a learning path, and continuously optimizes a recommendation strategy through an adaptive optimization algorithm; and finally, based on the learning result and the behavior data, evaluating the effectiveness of the learning path, and updating the knowledge point weight and recommendation strategy through a feedback mechanism, thereby realizing intelligent and self-adaptive personalized learning recommendation, the accuracy and adaptability of learning path recommendation can be effectively improved, learners are helped to master knowledge more efficiently, and the learning recommendation efficiency is improved. And the learning experience and effect are improved.

The invention discloses a collaborative personalized learning system and method based on a large model, and relates to the technical field of collaborative learning, and the system collects and fuses multi-modal data, generates a student state vector, plans a personalized learning path based on the state and a knowledge graph, and generates multi-modal explanation content according with the student style. The method comprises the following steps: extracting a student reasoning path, identifying thinking deviation, analyzing task performance, generating a path and content adjustment suggestion, identifying a motivation state, triggering an intervention strategy, receiving a teacher strategy, and realizing path and style co-construction. The multi-modal content of the matched style is generated, adaptive intervention is achieved in combination with inference analysis and emotion adjustment, and the learning efficiency and the teaching response intelligent level are improved.

The invention relates to a higher education personalized hierarchical teaching method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-dimensional learning data, carrying out the time-space alignment processing, and generating a synchronous multi-dimensional data set; performing spatial-temporal feature fusion and time sequence modeling on the data set by using a deep neural network, and constructing a dynamic student portrait; analyzing knowledge mastery degree features in the portrait through a semantic analysis model, and generating a personalized resource recommendation sequence in combination with the knowledge graph; based on the sequence and the portrait, planning a personalized learning path by using a path reasoning algorithm; and carrying out teaching hierarchy binding on the personalized resource recommendation sequence and the learning path to form a hierarchical teaching scheme. Accurate teaching support is provided for individual differences of students, and the teaching effect and learning experience are effectively improved.

The invention provides a learning performance evaluation-driven teaching management method constructed based on a capability graph. The method comprises the following steps: S1, constructing a multi-dimensional capability graph; s2, a step of operating a multi-modal data acquisition system; s3, performing a dynamic capability value calculation model; s4, a step of constructing a real-time capability early warning system; s5, generating a self-adaptive teaching strategy; s6, a step of carrying out interdisciplinary ability association analysis; s7, generating a personalized learning path; s8, dynamically evaluating the teaching effect; and S9, dynamically optimizing the teaching resources. According to the method, the core problems of data lag, inaccuracy in intervention and one-sided evaluation in traditional education management are solved by constructing a dynamic capability evaluation model, and a quantifiable decision support system is provided for precise teaching. The innovation of the method is that differential equation modeling and reinforcement learning are combined, and continuous optimization and adaptive adjustment of the education process are realized.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention discloses a personalized learning recommendation method for a learner. The method comprises the steps that the learner completes learning style evaluation and learning target setting of the learner; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of students in the platform learning process are analyzed to determine the personal basic condition of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt personalized learning features to the learning knowledge element sequence, a personalized learning push path recommendation mode is constructed, and the learning recommendation efficiency is improved. And the learning effect can be improved.

The invention discloses an educational scene-oriented knowledge graph enhanced large model personalized learning recommendation method and system, and the core thought of the method is that through the coupling of a knowledge dominant structure and a large model semantic capability, personalized learning resource pushing of triple driving of structure + semantics + behavior is realized. And deep, multi-dimensional and dynamic feedback-driven learning resource personalized intelligent recommendation can be realized in combination with student individual differences, learning paths, cognitive levels and teaching resource semantic structures.

A system for implementing blended learning, including: • a central processing unit (CPU) configured to receive, process and store data from a range of user interfaces, learning management software modules, classroom interaction devices and cloud-based data storage; • an AI engine integrated into the CPU, configured to analyze students' interactions with educational content, classroom activities, and performance data in real time, with the AI ​​engine applying machine learning techniques to create personalized learning paths for each student based on their individual learning style, past performance, and engagement patterns; • A range of devices for classroom interaction, including at least one smartboard, tablet, or interactive projector, each configured to capture real-time data on classroom activities such as student participation, oral responses, and collaborative tasks. The captured data is then transferred to the CPU for analysis by the AI ​​engine to refine and dynamically adapt learning paths for each student; • a student user interface, wherein the student interface displays dynamically generated content tailored to the student's progress and performance, and wherein the content includes multimedia materials such as videos, tests and reading material adapted based on the student's interaction history and learning needs; • a CPU-connected teacher interface, wherein the teacher interface displays real-time analytics and insights from student data, including performance trends, engagement levels, and potential learning gaps, and wherein the AI ​​engine provides the teacher with actionable recommendations for adapting teaching strategies, resources, and interventions based on the individual needs of the students and their performance in class; • a cloud-based storage system, whereby the system securely stores student profiles, historical performance data and learning content, and whereby the CPU retrieves, updates and stores data from the cloud-based system in real time to ensure continuous adaptation of the learning experience.

The invention provides a student adaptive auxiliary learning method and system based on artificial intelligence, and the method comprises the steps: constructing a subject knowledge graph, and carrying out the correlation and structuralization of knowledge points; the knowledge points are associated with learning resources and test questions in the subject knowledge graph; collecting learning behavior data of students, and constructing student portraits; the student portrait comprises three dimensions of learning style, knowledge level and hobbies and interests; wherein the knowledge level is a mastering probability of each knowledge point acquired according to the subject knowledge graph; personalized learning paths, learning resources and learning strategies are recommended to the students according to the student portraits and the subject knowledge maps; and learning results of the students are automatically evaluated and fed back. According to the characteristics and requirements of each student, a personalized learning scheme is provided, the learning efficiency is improved, the limitation of time and space is broken through, and the students can obtain high-quality learning resources which are more personalized for themselves anytime and anywhere.

The invention provides a wisdom education method and system based on student behavior sequence recommendation, and relates to the technical field of wisdom education. According to the method and the system, through an AI autonomous identification knowledge graph construction and innovative knowledge fusion method, a course knowledge system is structurally and hierarchically displayed, and a knowledge framework is clearly presented, so that knowledge graph framework construction of all-platform courses is facilitated, pre-repaired courses and subsequent courses are efficiently associated, and the construction efficiency is improved. And a clear and complete course learning path is provided for students. By adopting a recommendation algorithm based on a student behavior sequence, combining SASRec and introducing a self-attention mechanism of a sparse attention module, behavior data of students can be deeply analyzed, a learning path is adjusted according to real-time learning data, and personalized learning suggestions are provided for the students. According to the method and the system, multi-dimensional functions such as knowledge graph construction, a student behavior sequence recommendation algorithm and intelligent learning path optimization are integrated, and the method and the system have wide coverage and strong comprehensiveness.

The invention relates to the technical field of computers, in particular to a personalized learning recommendation method and device based on AI workflow and electronic equipment, after a user authorizes, learning behavior data of the user on a teaching platform is collected in real time, based on the collected data, the current learning state and ability level of the user are evaluated, the learning requirement is determined, and according to the learning requirement, the learning state and ability level of the user are recommended. According to the method, the difficulty encountered when a user learns course content is predicted, a course set suitable for current learning of the user is screened out from a preset course library according to a prediction result and learning requirements, and a personalized course recommendation list is generated in combination with interest preference and real-time learning behavior data of the user. Meanwhile, by collecting and analyzing feedback suggestions of the user on the recommended course and adjusting the recommendation strategy in real time according to the feedback, immediate feedback and recommendation adjustment on new questions or demands of the user are achieved.

The invention discloses an AIGC enabling auxiliary system for college second foreign language teaching and an implementation method. The system is cooperatively operated by five core modules; the intelligent voice recognition and user demand analysis module analyzes the learning demand of a learner by using a voice-to-text technology, and assists the customization of teaching content; the AIGC content generation module gathers multi-source data, generates personalized teaching resources by using a frontier model and continuously updates and evolves the personalized teaching resources; the learning progress management and evaluation module collects learning data, dynamically adjusts a personalized learning plan and provides evaluation and feedback; the real-time interaction and feedback module builds a multi-dimensional interaction channel, collects feedback of all parties, analyzes and processes the feedback and presents an application result; the multi-platform support module guarantees the stable operation of the system on a plurality of operating systems and devices, and efficiently completes data synchronization sharing and performance optimization adaptation. According to the invention, an all-around learning environment is established for second foreign language learners in universities, and the learning efficiency and experience are significantly improved.

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 discloses a language learning assistance application system based on speech recognition, and belongs to the technical field of learning assistance. The system comprises an intelligent voice acquisition and enhancement module for realizing acquisition and preprocessing of high-quality voice signals in a complex environment; the multi-modal feature extraction module dynamically extracts and fuses multi-dimensional learning feature information, completes feature analysis and boundary segmentation of a phoneme level, and is responsible for feature normalization and optimization; the deep speech recognition module is used for realizing high-precision speech recognition based on the normalized features; the intelligent evaluation engine module is used for realizing real-time and multi-dimensional evaluation on the language performance of the learner; the personalized learning management module integrates the evaluation results for dynamically planning an optimal learning path for the learner, manages the learning progress, and provides learning effect prediction and intervention suggestions at the same time; and the learning interaction experience module is responsible for visual presentation of learning feedback, multi-modal man-machine interaction design and user interface optimization.

The invention discloses a teaching display system and method based on a knowledge graph, and a medium, and relates to the technical field of artificial intelligence and education, and the method comprises the steps: regularly collecting multi-modal original teaching data, and constructing a teaching knowledge graph; dynamically updating the relation weight of the teaching knowledge graph; according to the potential capability coefficient of each learning terminal, the distinction degree coefficient and the difficulty coefficient of the plurality of questions corresponding to each knowledge point and the relationship weight between the knowledge points, obtaining a cognitive dependency coefficient between the knowledge points corresponding to each learning terminal; generating a personalized learning path of each learning terminal according to the cognitive dependency coefficient and the learning target; performing interaction attribute addition on nodes in the teaching knowledge graph according to the personalized learning path, and generating a VR virtual scene and visual data; and real-time learning monitoring is performed on the personalized learning path of each learning terminal, and real-time learning feedback operation is performed according to the real-time learning monitoring result, so that the overall teaching quality is remarkably improved.

The invention relates to the technical field of computers, in particular to a multi-mode-based three-dimensional model editing method and device, equipment and a medium. The method comprises the following steps: acquiring a multi-modal fusion image personalized generation method and a source three-dimensional model; obtaining a personalized image generation network corresponding to the source three-dimensional model, and training the personalized image generation network; based on the reference image list and the corresponding text description, multi-modal embedded information is obtained after fusion; taking the trained personalized image generation network as a skeleton, and adding a multi-personalized learning module to obtain a multi-personalized image editing network; and editing the source three-dimensional scene image according to the multi-modal embedded information by using a multi-personalized image editing network to obtain a single-view-angle edited image. The multi-mode embedded information generated by the method allows a user to input prompt information of multiple modes such as texts, images, sketches and the like, complementarity of prompt of each mode is mined, so that controllability of editing is improved, and high-quality three-dimensional model editing is realized.

The invention discloses an intelligent personalized learning path recommendation system, and relates to the technical field of learning systems, and the system comprises a multi-source data collection module which is used for synchronizing behavior data of students in a cross-platform learning scene in real time; the cognitive feature analysis engine comprises a style recognition sub-engine and a demand prediction sub-engine to predict the potential learning demand intensity of students for unmastered knowledge points and generate a priority list comprising knowledge gaps; the personalized path generator generates a three-dimensional path plan; the proportion of guided questioning, example demonstration and autonomous exploration is automatically configured according to knowledge difficulty; the dynamic self-adaptive adjustment unit is used for designing a reward function including short-term progress speed and long-term ability growth potential by taking real-time performance of students as a state space and taking path adjustment action as a decision space based on a reinforcement learning framework; and a double-loop feedback mechanism is realized. According to the method, the dimension limitation of traditional learning analysis is broken through, the difficulty of stiffness of a static course template is broken through, and meanwhile, the semantic gap of subject cognition is broken through.

The invention discloses an interactive processing method and device based on course management, and relates to the technical field of intelligent education, and the method comprises the steps: collecting student information, carrying out the preprocessing of the student information, and generating an initial learning path according to the preprocessed student information; in the process that the student learns according to the initial learning path, learning behavior data of the student is monitored, and a personalized learning path is generated according to the learning behavior data of the student; monitoring emotion change data, cognitive load data and attention level data of the trainee in the personalized learning path in real time, constructing a learning state evaluation model, and evaluating the current learning state of the trainee; and dynamically generating an immersive learning situation according to the current learning state of the student. According to the invention, the pertinence and utilization efficiency of educational resources are improved, and the enthusiasm and effect of learning are enhanced.

The invention provides an online learning energy concentration degree evaluation method combined with formability evaluation, and belongs to the technical field of online education, and the method comprises the steps: capturing eye movement data of a learner in an online learning process, carrying out feature extraction and time sequence analysis, and combining formability evaluation data to construct a deep neural network energy concentration degree evaluation model. And performing real-time dynamic evaluation on the energy concentration state of the learner by using multi-dimensional indexes such as attention continuity, visual concentration degree and cognitive input degree. Through a sliding time window technology and a specially-designed evaluation equation, the score of the energy concentration degree of each learning stage can be accurately calculated, and finally the overall energy concentration level of the learner is comprehensively output, so that data support is provided for personalized learning. According to the method, learning energy concentration degree monitoring and analysis support is provided, and the problem that it is difficult to continuously, objectively and comprehensively monitor the attention state of a learner on the premise that learning activities are not affected in the prior art is solved.

The invention relates to a personalized learning resource recommendation method based on a subject knowledge graph and a graph neural network, and belongs to the field of education content recommendation. The method comprises the following steps: extracting key entities in a textbook and a relationship between the key entities, and constructing a subject knowledge graph; establishing a preliminary learning record table according to the behavior data of the student in the learning process; through knowledge tracking model integration, predicting mastery degree distribution of students for each knowledge point in the learning record sheet, and constructing student portraits; embedding the knowledge points in the subject knowledge graph and the association relationship thereof into the student portrait, and updating the knowledge embedding representation in the student portrait through a graph neural network; and constructing a personalized recommendation model, and pushing learning resources conforming to the current learning state of the student by adopting a mixed recommendation method of collaborative filtering and content recommendation. According to the method, personalized demands of students can be accurately captured in multiple dimensions, and the accuracy, generalization and interpretability of recommended contents are effectively improved.

The invention discloses a preschool education field knowledge base construction method fusing a knowledge graph and a large language model, and relates to the technical field of preschool education method optimization. Multi-channel collection of preschool education data and cleaning preprocessing; utilizing a customized model to extract entities and relationships, and constructing a structured knowledge graph; performing fine tuning on the big language model in the preschool education field; fusing atlas and model knowledge through a gating mechanism, and performing dual verification; establishing a monthly updating mechanism to realize incremental learning; in addition, personalized knowledge recommendation is realized based on a user portrait, interactive questions and answers are optimized through combination of retrieval and a model, and data security is guaranteed by adopting anonymization and authority control. Through multi-source data integration and intelligent technology fusion, an accurate and dynamic preschool education knowledge base is constructed, requirements of children are more accurately understood, content fitting cognition is generated, personalized learning recommendation is realized, interaction experience and knowledge service quality are improved, data security is guaranteed, and digital upgrading of the preschool education field is promoted.

The invention relates to the technical field of art education data analysis, and discloses an AI-enabled data-driven art education resource recommendation engine, which is characterized in that user behaviors, resource metadata and context environment data are acquired through a multi-source data acquisition module; the user portrait construction module constructs a multi-dimensional user portrait by using a deep clustering algorithm and an attention mechanism; the resource feature extraction module extracts resource features by means of a graph neural network; the privacy protection processing module uses differential privacy and federated learning to protect data privacy; and the dynamic recommendation generation module generates a personalized recommendation list based on reinforcement learning. The recommendation engine can accurately recommend art education resources meeting user requirements, data privacy is guaranteed, resource association is mined, recommendation strategies are optimized, the quality and effect of art education resource recommendation are effectively improved, and personalized learning requirements of users are met.

The invention relates to the technical field of intelligent teaching, in particular to an intelligent teaching system for traditional Chinese medicine clinical skill cultivation. The system comprises a virtual reality simulation module, an operation interaction module, an operation data analysis module, an adaptive learning feedback module and a teaching evaluation module. The virtual reality simulation module constructs a vivid traditional Chinese medicine skill training scene through three-dimensional modeling, ray tracing and physical simulation technologies, and the vivid traditional Chinese medicine skill training scene covers pulse feeling, acupuncture and moxibustion, massage and other operations. The operation interaction module collects user operation data and extracts features in combination with a variational auto-encoder. The operation data analysis module analyzes user operation deviation by using a graph neural network and a generative adversarial network, the adaptive learning feedback module generates a personalized learning strategy, and the teaching evaluation module evaluates a learning effect through fuzzy evaluation and analytic hierarchy process. According to the invention, intelligence, individuation and high efficiency of traditional Chinese medicine clinical skill cultivation are realized, the fidelity of teaching and the accuracy of feedback are improved, and the skill level and the training effect of learners are also improved.

The invention discloses a learning assistance method and device based on a question bank knowledge graph, a storage medium and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: firstly, carrying out the extraction of knowledge points based on a textbook text, and obtaining a basic knowledge point cluster; performing importance degree evaluation on knowledge points in the basic knowledge point cluster, and performing screening according to an evaluation result to obtain an important knowledge point cluster; determining knowledge point distribution information of a basic knowledge point cluster and an important knowledge point cluster in the textbook, generating a knowledge tree containing the knowledge point distribution information, and determining association strength among knowledge points according to the number of branches of the knowledge tree; constructing a question bank knowledge graph according to the association strength among the knowledge points; and matching the question bank knowledge graph with a learning dialogue record of the user, and monitoring a question brushing condition of the user according to a matching result. An important knowledge point cluster is extracted from a teaching material, and a question bank knowledge graph is constructed, so that personalized learning assistance is realized.

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

Systems and methods for developing personalized instructional content include a computing device using software modules which capture data about subjects, teachers, and learners, train artificial intelligence models based on the acquired data, use the artificial intelligence models to generate instructional content personalized to an instructor and / or learner based their input, compare the generated instructional content to vetted sources and correct errors, and cause the processor to output a personalized instructional course including the updated generated instructional materials in the format capable of display to an individual learner.

The invention discloses an artificial intelligence self-adaptive education system based on big data, and relates to the field of intelligent education, the artificial intelligence self-adaptive education system comprises an education resource management module, a student data management module, a self-adaptive learning recommendation module, an auxiliary teaching decision module and a security reinforcement module, and a cloud data management library is used for classifying and integrating education resources. The method comprises the steps of establishing a knowledge graph, performing knowledge point classification on resources by constructing the knowledge graph, performing accurate matching on personal portraits of students and the knowledge graph through an incremental self-adaptive matching model, dynamically adjusting recommended learning resources in real time, analyzing collective learning problems through a big data intelligent analysis model, and generating collective teaching suggestions. A three-layer security firewall is adopted to ensure the security of the system; the problem that an existing self-adaptive education system neglects the learning progress and weak links of classes or groups is solved, and intelligent management, personalized learning recommendation and collective problem analysis of education resources can be achieved.