Intelligent education content management method and system based on knowledge graph

Abstract

The invention relates to the technical field of knowledge graph and content management, in particular to an intelligent education content management method and system based on a knowledge graph, and the method comprises the steps: obtaining multi-modal education content data which comprises text data and audio data; based on the multi-modal education content data, executing multi-modal tensor decomposition to obtain unified feature representation; according to the unified feature representation, topological continuous coherence feature extraction is executed to obtain topological features; based on the topological features, quantum entanglement feature enhancement is executed, and enhanced features are obtained; executing non-exchangeable geometric feature fusion according to the enhanced features to obtain fusion features; based on the fusion features, chaotic dynamics knowledge extraction is executed, and extracted knowledge is obtained; according to the method, the extracted knowledge is constructed into the knowledge graph, different forms of education contents such as texts and audios are integrated through a multi-modal tensor decomposition technology, complementary information among different modals can be captured, and comprehensiveness and accuracy of knowledge representation are improved.

Description

Technical Field

[0001] The present invention relates to the field of knowledge graphs and content management technology, and more specifically, to an intelligent education content management method and system based on knowledge graphs. Background Art

[0002] With the rapid development of educational informatization, intelligent educational content management systems play an increasingly important role in modern education. Such systems aim to efficiently organize, retrieve and recommend educational resources and provide learners with personalized learning experiences. However, despite the progress made in current technology, there are still many problems that need to be solved.

[0003] At present, the most advanced educational content management systems are mainly based on natural language processing and machine learning technologies. These systems usually use methods such as bag-of-words model, TF-IDF or word embedding to extract text features, and use various classification or clustering algorithms to organize knowledge structures. In terms of knowledge graph construction, existing technologies mostly rely on predefined ontology structures or relationship extraction methods based on co-occurrence statistics. For personalized recommendations, common methods include collaborative filtering and content-based recommendation algorithms.

[0004] However, these existing technologies still face significant challenges when dealing with complex educational content. First, most systems can only process data of a single modality (usually text) and cannot effectively utilize the rich information in multimodal educational resources (such as video explanations, audio materials, etc.). Secondly, existing feature extraction methods often find it difficult to capture the deep connections and structured information between knowledge, resulting in the constructed knowledge graph being incomplete and inaccurate. In addition, in terms of personalized recommendations, existing algorithms find it difficult to fully consider the learner's knowledge status and learning goals, and the accuracy of the recommendation results is limited. Finally, with the rapid increase in educational resources, the response speed and scalability of the system have also become a prominent issue.

[0005] These limitations seriously affect the actual application effect of intelligent educational content management systems. Learners may not be able to obtain the most suitable learning resources for themselves, and educators may find it difficult to fully understand and effectively organize teaching content. Therefore, there is an urgent need for new intelligent educational content management methods that can comprehensively process multimodal data, deeply understand knowledge structure, accurately recommend learning paths, and maintain efficient performance. Summary of the invention

[0006] The present invention is an innovative solution to the above technical problems. The present invention aims to develop an intelligent education content management method and system based on knowledge graph, which can effectively process multimodal education data, build a more complete and accurate knowledge graph, provide highly personalized learning recommendations, and maintain excellent system performance.

[0007] The present invention provides an intelligent educational content management method based on knowledge graph, comprising:

[0008] The acquisition steps include:

[0009] Acquiring multimodal educational content data, wherein the multimodal educational content data includes text data and audio data;

[0010] Processing steps include:

[0011] Based on the multimodal educational content data, performing multimodal tensor decomposition to obtain a unified feature representation;

[0012] According to the unified feature representation, topological continuous homology feature extraction is performed to obtain topological features;

[0013] Based on the topological features, quantum entanglement feature enhancement is performed to obtain enhanced features;

[0014] According to the enhanced features, performing non-commutative geometric feature fusion to obtain fused features;

[0015] Based on the fusion features, chaotic dynamics knowledge extraction is performed to obtain extracted knowledge;

[0016] Output steps include:

[0017] The extracted knowledge is constructed into a knowledge graph, and the knowledge graph is output.

[0018] Preferably, the performing multimodal tensor decomposition specifically includes:

[0019] Constructing a multi-order input tensor, wherein the multi-order input tensor represents multi-modal data;

[0020] Performing tensor decomposition on the multi-order input tensor to obtain a eigenvector of each mode;

[0021] Based on the feature vector, the unified feature representation is generated.

[0022] Preferably, the performing of topological continuous homology feature extraction specifically includes:

[0023] constructing a simplicial complex based on the unified feature representation;

[0024] computing the persistent homology group of the simplicial complex;

[0025] generating a persistence graph, the persistence graph representing birth and death times of homology classes;

[0026] The topological features of the persistence graph are analyzed to obtain the topological features.

[0027] Preferably, the performing of quantum entanglement feature enhancement specifically includes:

[0028] Mapping the topological features to a quantum bit space to obtain a quantum state representation;

[0029] Applying quantum gate operations to the quantum state representation to create a multi-signature entangled state;

[0030] Based on the multi-signature entangled state, the enhanced feature is generated.

[0031] Preferably, the performing of non-commutative geometric feature fusion specifically includes:

[0032] Construct noncommutative C*-algebras;

[0033] defining a spectral triple, the spectral triple comprising the non-commutative C*-algebra, the Hilbert space, and the Dirac operator;

[0034] Based on the spectral triples, feature fusion is performed to obtain the fused features.

[0035] Preferably, the performing of chaotic dynamics knowledge extraction specifically includes:

[0036] Construct feature-based chaotic mapping networks;

[0037] Iteratively executing the chaotic mapping network to obtain a chaotic system;

[0038] The strange attractor of the chaotic system is analyzed, and a stable knowledge structure is extracted to obtain the extracted knowledge.

[0039] As a preference, it also includes:

[0040] Based on the knowledge graph, perform educational content retrieval;

[0041] Generate a personalized learning path based on the search results.

[0042] Preferably, the performing of educational content retrieval specifically includes:

[0043] Receive user query requests;

[0044] Mapping the user query request to entities and relationships in the knowledge graph;

[0045] Based on a graph traversal algorithm, performing multi-hop reasoning in the knowledge graph;

[0046] Based on the inference results, relevant educational content is returned.

[0047] Preferably, generating a personalized learning path specifically includes:

[0048] Obtain user learning history and learning goals;

[0049] Constructing a user knowledge state based on the knowledge graph and the user learning history;

[0050] Using a reinforcement learning algorithm, finding an optimal learning path in the knowledge graph;

[0051] The optimal learning path is output as a personalized learning path.

[0052] The intelligent educational content management system based on knowledge graph for executing the method comprises:

[0053] A data acquisition module, used to acquire multimodal educational content data, wherein the multimodal educational content data includes text data and audio data;

[0054] Feature processing module for:

[0055] Based on the multimodal educational content data, performing multimodal tensor decomposition to obtain a unified feature representation;

[0056] According to the unified feature representation, topological continuous homology feature extraction is performed to obtain topological features;

[0057] Based on the topological features, quantum entanglement feature enhancement is performed to obtain enhanced features;

[0058] According to the enhanced features, performing non-commutative geometric feature fusion to obtain fused features;

[0059] A knowledge extraction module, used for performing chaotic dynamics knowledge extraction based on the fusion features to obtain extracted knowledge;

[0060] A knowledge graph construction module, used to construct the extracted knowledge into a knowledge graph;

[0061] The content management module is used to perform educational content retrieval and personalized learning path generation based on the knowledge graph.

[0062] Specifically, the beneficial effects of the present invention are mainly reflected in the following aspects:

[0063] First, in terms of knowledge representation, the present invention effectively integrates educational content in different forms such as text and audio through multimodal tensor decomposition technology. This method can capture complementary information between different modalities, greatly improving the comprehensiveness and accuracy of knowledge representation. For example, when processing video courses, the system can not only understand the content of the explanation, but also extract key points and emotional information from the teacher's voice, thereby building a richer knowledge structure.

[0064] Secondly, in terms of knowledge structure analysis, the topological persistent homology feature extraction method introduced in this paper provides a new perspective for understanding the deep-level associations between knowledge. This method can identify knowledge structures that are difficult to discover with traditional algorithms, such as circular dependencies or interdisciplinary knowledge links. This not only helps to build a more complete knowledge graph, but also provides a basis for designing a more reasonable learning path.

[0065] In terms of feature enhancement and fusion, the present invention draws on the concepts of quantum entanglement and non-commutative geometry to develop unique feature processing methods. These methods can capture the nonlinear relationships and multi-level structures between knowledge points, greatly improving the expressive power of knowledge representation. For example, the system can better understand the transformation process of mathematical concepts in physics applications, or the changes in the interpretation of literary works in different historical contexts.

[0066] In terms of knowledge extraction and organization, the chaotic dynamics method adopted by the present invention provides an innovative solution for extracting stable knowledge structures from seemingly disordered data. This method is particularly suitable for dealing with interdisciplinary and rapidly updated knowledge fields, and can identify core knowledge points and key connections from massive and changing educational resources.

[0067] Finally, in terms of system performance and user experience, although the present invention introduces a complex mathematical model, it actually improves the response speed and scalability of the system through the optimization algorithm. This means that even when processing large-scale educational data, the system can still maintain efficient retrieval and recommendation performance, providing users with a smooth learning experience.

[0068] In general, the method of the present invention not only achieves a breakthrough in technology, but also shows great potential in practical application. It provides a solid technical foundation for building a smarter and more personalized education system, and is expected to play an important role in improving learning efficiency, promoting personalized education, and supporting lifelong learning. Through this innovative method, we can expect to see more efficient use of educational resources, more personalized learning experience, and ultimately promote the overall improvement of education quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 The figure is a flow chart of the method of the present invention.

[0070] Figure 2 It is the overall logic block diagram of the system of the present invention.

[0071] Figure 3 It is a logic block diagram of the feature processing module of the present invention. DETAILED DESCRIPTION

[0072] Please refer to Figure 1-3The present invention provides an intelligent educational content management method and system based on knowledge graph. The method aims to achieve intelligent management and personalized recommendation of educational content through advanced mathematical and algorithmic techniques. The specific implementation methods of the present invention will be described in detail below.

[0073] The present invention provides an intelligent educational content management method based on a knowledge graph, comprising an acquisition step, a processing step and an output step. In the acquisition step, the method acquires multimodal educational content data, which includes text data and audio data. For example, the text data may be teaching material content, a course outline or student notes, and the audio data may be a course recording or an audio track of a teaching video.

[0074] In the processing step, the method first performs multimodal tensor decomposition based on the acquired multimodal educational content data to obtain a unified feature representation. The purpose of this step is to unify data of different modalities into a common feature space. Preferably, the present invention adopts the following tensor decomposition algorithm:

[0075]

[0076] in, is an N-order input tensor, representing multimodal data. r are singular values, is the eigenvector of each mode, Represents the vector outer product. Through this decomposition, the interaction between different modes can be captured and a unified representation can be provided for subsequent processing.

[0077] Through tensor decomposition, the system can fuse data from different modalities such as text and audio into a unified feature representation. This helps to understand educational content more comprehensively and avoid the limitations of single modality information. Tensor decomposition can effectively reduce the dimensionality of data, reduce storage and computing costs, while retaining key information. This is especially important for the processing of large-scale educational data. By capturing the interactions between different modalities, tensor decomposition can generate more representative feature representations, thereby improving the performance of subsequent tasks (such as recommendation and retrieval).

[0078] In educational scenarios, the relationship between knowledge points is often not a simple linear structure, but a complex dependency. For example, some knowledge points may be the basis of other knowledge points, or there may be circular dependencies between multiple knowledge points. Through topological persistence, we can capture this complex structured information and transform it into features that are easy to understand and process.

[0079] Next, this method performs topological continuous homology feature extraction based on the unified feature representation to obtain topological features. This step uses the continuous homology theory in topological data analysis to help understand the essential structure of the data. Specifically, the continuous homology group is defined as:

[0080]

[0081] in, is the boundary operator of the k-dimensional simplicial complex. Based on this, we construct a persistence graph:

[0082] PD k ={(b i ,d i )|i∈I},

[0083] Among them, (b i ,d i ) represents the birth and extinction time of the i-th k-dimensional homology class. By analyzing this persistence graph, we can obtain the topological characteristics of the data, which often reflect the structural information of educational content.

[0084] Through topological persistence homology, the system can automatically discover structured information in educational content, such as dependencies between knowledge points, circular dependencies, etc. This helps to better organize and present educational content and improve learning efficiency. Long-lived homology classes in persistence graphs usually correspond to core concepts or important topics in educational content. By analyzing these features, the system can identify key knowledge points throughout the course, helping teachers and students to better grasp the key points. Topological features can provide an important basis for the generation of personalized learning paths. For example, if a student has some unmastered knowledge points in his knowledge state graph, the system can recommend a suitable learning path for him based on these topological features to ensure the coherence and effectiveness of the learning process.

[0085] The system first constructs a simplicial complex based on the unified feature representation, treating each knowledge point as a vertex and the associations between knowledge points as edges. For example, if two knowledge points often appear together in the textbook, or have a dependency in the order of learning, the system will add an edge between them. Next, the system calculates the persistence homology group of the simplicial complex and obtains the topological features of each dimension. Then, the system generates a persistence graph to record the birth and extinction times of the homology classes. Finally, the system analyzes the topological features in the persistence graph, identifies stable knowledge structures, and uses them for subsequent content organization and learning path planning.

[0086] In educational scenarios, the relationships between knowledge points are often complex, especially when multiple related concepts are involved. Traditional linear models may not be able to fully capture these relationships. By introducing quantum entanglement, the system can represent the complex associations between knowledge points at the quantum level, thereby generating a more accurate feature representation.

[0087] Subsequently, this method performs quantum entanglement feature enhancement based on topological features to obtain enhanced features. This innovative step draws on the concept of entanglement in quantum computing and aims to capture the nonlinear relationship between features. First, define the two-particle entangled state:

[0088]

[0089] Then, map the features to the qubit space:

[0090] f i >=α i |0>+β i |1>,

[0091] Through quantum gate operations, multi-characteristic entangled states are created:

[0092]

[0093] This entangled state can effectively represent the complex conceptual associations in educational content.

[0094] Quantum entanglement can effectively capture the nonlinear relationships between knowledge points, especially when multiple related concepts are involved. This helps to generate richer feature representations and improve the performance of subsequent tasks. Through quantum entanglement, the system can represent the complex associations between knowledge points at the quantum level and generate more accurate feature representations. This helps to improve the accuracy of tasks such as personalized recommendations and knowledge extraction. Quantum entanglement provides new ideas for feature enhancement, especially when dealing with complex, nonlinear relationships. This enables the system to achieve a higher level of intelligence in educational content management.

[0095] The system first maps the topological features to the quantum bit space to obtain the quantum state representation. Specifically, for each topological feature f i , the system defines a mapping function where θ i and φ i is a parameter determined based on the eigenvalue. Next, the system applies quantum gate operations to the quantum state representation to create a multi-signature entangled state. For example, for two qubits, the system can apply a combination of CNOT gates and Hadamard gates to generate an entangled state. Finally, the system generates enhanced features based on the multi-feature entangled state and uses them for subsequent tasks.

[0096] Next, the method performs non-commutative geometric feature fusion based on the enhanced features to obtain fused features. This step draws on the idea of ​​non-commutative geometry and can better handle the hierarchy and interdependence in educational content. Define the non-commutative C*-algebra A and construct the spectral triple (A, H, D), where H is the Hilbert space and D is the Dirac operator. Feature fusion can be expressed as:

[0097] f fused =Tr(π(a)F(D)),

[0098] Among them, π is the representation of A on H, and F is an appropriately selected function. This fusion method can preserve the complex relationship between features and help to more accurately represent the semantic information of educational content.

[0099] Finally, this method performs chaotic dynamics knowledge extraction based on fusion features to obtain extracted knowledge. This step uses chaos theory to extract stable knowledge structures from seemingly disordered data. Define Logistic mapping:

[0100] x n+1 =rx n (1-x n ),

[0101] Among them, r is the control parameter. Based on this, the characteristic chaotic mapping network is constructed:

[0102] X n+1 =F(X n ,r),

[0103] Among them, X n is the eigenvector, and F is a nonlinear function. By analyzing the strange attractors of this chaotic system, stable knowledge structures can be extracted, which reflect the core concepts and key knowledge points in the educational content.

[0104] In the output step, the method constructs the extracted knowledge into a knowledge graph and outputs the knowledge graph. This knowledge graph not only contains the entities and relationships of the educational content, but also contains the deep semantic information and structured knowledge obtained through the above complex processing.

[0105] The method of the present invention specifically includes the following steps when performing multimodal tensor decomposition: first, construct a multi-order input tensor, which represents multimodal data. For example, for educational content containing text and audio, a three-order tensor can be constructed, in which the first dimension represents different educational resources, the second dimension represents text features, and the third dimension represents audio features.

[0106] Next, the multi-order input tensor is decomposed to obtain the eigenvector of each mode. In practice, methods such as Tucker decomposition or CP decomposition can be selected. Preferably, the present invention adopts Tucker decomposition, which is in the form of:

[0107]

[0108] in, is the core tensor, A (i) is the factor matrix of the i-th mode. This decomposition method can effectively capture the interaction between different modes.

[0109] Finally, a unified feature representation is generated based on the obtained feature vectors. This can be achieved by connecting or fusing feature vectors of different modalities. For example, a weighted summation method can be used:

[0110] f unified =w 1 f text +w 2 f audio ,

[0111] Among them, w 1 and w 2 is a weight parameter, and the optimal value can be determined by cross-validation. Usually, w can be set 1 =0.6 and w 2 =0.4, because in educational content, text information often carries more semantic information than audio information.

[0112] When performing topological continuous homology feature extraction, the present invention specifically includes the following steps: First, based on the unified feature representation, a simplicial complex is constructed. In the scenario of educational content management, each knowledge point can be regarded as a vertex, and the association between knowledge points can be regarded as an edge. For example, if two knowledge points often appear together in the textbook, or there is a dependency relationship in the learning order, an edge is added between them.

[0113] Next, the persistent homology group of the simplicial complex is calculated. This step can help understand the topological structure of educational content. For example, the 0-dimensional homology group reflects the clustering of knowledge points, and the 1-dimensional homology group reflects the cyclic dependencies in the knowledge system.

[0114] Then, a persistence graph is generated, which represents the birth and extinction time of homology classes. In the context of educational content, birth can be understood as the introduction of a concept or topic, and extinction can be understood as the concept being included or replaced by higher-level knowledge.

[0115] Finally, the topological features of the persistence graph are analyzed to obtain the topological features. We can focus on features with longer duration, which often represent the core concepts or key knowledge structures in the educational content. For example, if a long-lived 1-dimensional homology class is observed in the persistence graph, it may represent an important topic that runs through the entire course.

[0116] Through this method, not only can the structured features of educational content be extracted, but also the overall topological structure of the knowledge system can be gained, providing an important basis for subsequent content organization and learning path planning. In a preferred embodiment of the present invention, the step of performing quantum entanglement feature enhancement specifically includes the following process: First, the topological features are mapped to the quantum bit space to obtain a quantum state representation. The purpose of this step is to convert classical information into quantum information in order to take advantage of quantum computing. Specifically, for each topological feature f i , we can define a mapping function Φ:

[0117]

[0118] Among them, θ i and φ i is a parameter determined by the eigenvalue. For example, you can set φ i =2πf i This mapping method ensures that the characteristic information is fully encoded into the quantum state.

[0119] Next, quantum gate operations are applied to the quantum state representation to create a multi-feature entangled state. In the context of educational content management, the significance of this step is to capture the complex associations between different knowledge points or concepts. The present invention preferably uses a combination of a control-NOT (CNOT) gate and a Hadamard gate to create an entangled state. For example, for two quantum bits, the following quantum circuit can be applied:

[0120]

[0121] Among them, θ i and φ i is a parameter determined by the eigenvalue. For example, you can set φ i =2πf i This mapping method ensures that the characteristic information is fully encoded into the quantum state.

[0122] In educational scenarios, the dependencies between knowledge points are often asymmetric. For example, some knowledge points may be prerequisites for other knowledge points, but the reverse is not necessarily true. By introducing non-commutative geometry, the system can better handle these asymmetric relationships and generate more accurate feature representations.

[0123] We can also define an operator a ij , represents the degree of dependence from knowledge point i to knowledge point j. Obviously, a ij Not necessarily equal to a ji , thus forming a non-commutative algebraic structure. In the non-commutative geometric feature fusion, the following fusion formula is used:

[0124] D=∑ i,j a ij |i> <j|+a ji |j> <i|,

[0125] f fused =Tr(π(a)F(D)),

[0126] where π is the representation of the C*-algebra on the Hilbert space H, and F is an appropriately chosen function, for example, F(x) = (1 + x 2 ) -1 / 2 The advantage of this fusion approach is that it can naturally incorporate asymmetric relational information, which is particularly important in the organization of educational content because learning paths are often directional.

[0127] Non-commutative geometry can naturally handle asymmetric dependencies between knowledge points, especially when the learning path is directional. This helps to generate more accurate feature representations and improve the effect of personalized learning path planning. By constructing non-commutative C*-algebras and spectral triples, the system can naturally fuse the complex relationship information between knowledge points and generate richer feature representations. This helps to improve the performance of subsequent tasks, such as knowledge extraction and recommendation. Non-commutative geometry provides new ideas for learning path planning, especially when dealing with asymmetric dependencies. This enables the system to generate a more personalized learning path for each learner, ensuring the coherence and effectiveness of the learning process.

[0128] The system first constructs a non-commutative C*-algebra to represent the asymmetric dependencies between knowledge points. Specifically, the system defines an operator a ij , represents the degree of dependence from knowledge point i to knowledge point j. Next, the system defines the spectral triple (A, H, D), where A is a non-commutative C*-algebra, H is a Hilbert space, and D is a Dirac operator. Through the feature fusion formula f fused =Tr(π(a)F(D)), the system can naturally fuse these asymmetric relationship information, generate fused features, and use them for subsequent tasks.

[0129] The method of the present invention adopts an innovative method when performing chaotic dynamics knowledge extraction. Specifically, it includes the following steps: First, construct a feature-based chaotic mapping network. The present invention uses an improved Logistic mapping as the basis, which is defined as follows:

[0130] X n+1 =rX n (1-X n )+εsin(2πX n ),

[0131] Among them, X n is the eigenvector at time n, r is the control parameter, and ε is a small disturbance term. This improved mapping introduces periodic disturbances, which can better simulate the periodic and nonlinear characteristics of educational content.

[0132] Next, the chaotic mapping network is iteratively executed to obtain a chaotic system. In practice, hundreds to thousands of iterations are usually performed to ensure that the system reaches a stable state. It is worth noting that the selection of the number of iterations requires a balance between computational cost and result stability. The present invention suggests that for general educational content management tasks, 1000 iterations can usually achieve good results.

[0133] In educational scenarios, educational content often has periodic and nonlinear characteristics. For example, certain knowledge points may appear repeatedly in different courses, or the relationship between certain concepts is nonlinear. By introducing chaotic dynamics, the system can extract stable knowledge structures from these seemingly disordered data, helping teachers and students better understand educational content.

[0134] Finally, the strange attractor of the chaotic system is analyzed, and the stable knowledge structure is extracted to obtain the extracted knowledge. The present invention uses the Grassberger-Procaccia algorithm to analyze the dimension of the strange attractor and use this dimension to characterize the complexity of the knowledge structure. Specifically, the correlation integral is calculated:

[0135]

[0136] Where H is the Heaviside step function and r is a small distance threshold. By analyzing the change of C(r) with r, the dimension of the strange attractor can be estimated, thereby quantifying the complexity of the knowledge structure.

[0137] The system first constructs a feature-based chaotic mapping network and defines an improved Logistic mapping X n+1 =rX n (1-X n )+εsin(2πX n ), where X nis the eigenvector at time n, r is the control parameter, and ε is a small disturbance term. Next, the system iteratively executes the chaotic mapping network to build a chaotic system. By analyzing the strange attractor of the system, the system can extract stable knowledge structures, calculate the correlation integral C(r), and estimate the dimension of the strange attractor. This helps to quantify the complexity of the knowledge structure and provides an important basis for subsequent tasks.

[0138] Through chaotic dynamics, the system can extract stable knowledge structures from seemingly disordered data, helping teachers and students better understand educational content. This helps improve the accuracy of knowledge extraction and improve learning efficiency. Chaotic dynamics can simulate the periodic and nonlinear characteristics in educational content, helping the system better capture these complex behaviors. This helps to generate more accurate feature representations and improve the performance of subsequent tasks. By analyzing the dimensions of strange attractors, the system can quantify the complexity of knowledge structures, helping teachers and students better understand the difficulty and depth of educational content. This helps to provide an important basis for the generation of personalized learning paths.

[0139] The method of the present invention is not limited to knowledge extraction, but also further extends to the application level of educational content. Based on the constructed knowledge graph, the method also includes the step of performing educational content retrieval. The purpose of this step is to enable users to quickly and accurately find the required educational resources. Specifically, the present invention adopts a retrieval method based on a graph neural network.

[0140] First, a user query request is received. This request may be a keyword, a phrase, or a complete question. For example, the user may enter what is Newton's second law or the application of calculus in physics.

[0141] Then, the user query request is mapped to entities and relations in the knowledge graph. This step uses advanced natural language processing techniques, including named entity recognition and relationship extraction. For example, for the query Newton's second law, the system will identify the two entities Newton and the second law, as well as the proposed relationship between them.

[0142] Next, based on the graph traversal algorithm, multi-hop reasoning is performed in the knowledge graph. The present invention adopts an improved random walk algorithm, which can efficiently explore relevant knowledge points in the knowledge graph. Specifically, an importance score S(v) is defined:

[0143]

[0144] Where N(v) is the set of neighbors of node v, α is a balancing parameter (usually set to 0.85), and R(v) is the relevance of node v to the query. This algorithm can effectively balance global importance and query relevance.

[0145] Finally, the relevant educational content is returned based on the inference results. The present invention not only returns directly matched content, but also recommends relevant prior knowledge and advanced content based on the structure of the knowledge graph, thereby providing users with a more comprehensive learning experience.

[0146] Through this method, the present invention realizes intelligent educational content retrieval, which can not only accurately answer users' queries, but also provide personalized knowledge recommendations, greatly improving learning efficiency and experience. In another preferred embodiment of the present invention, the method also includes the step of generating a personalized learning path. The purpose of this step is to provide each learner with a learning plan that best suits their personal situation, thereby maximizing the learning effect. Specifically, generating a personalized learning path includes the following process:

[0147] First, obtain the user's learning history and learning goals. The learning history may include information such as the knowledge points that the user has mastered, learning progress, and weak links. The learning goal may be a specific knowledge point or skill, or it may be a more macro learning direction. For example, a high school student's learning goal may be to master the basics of calculus, but his learning history may show that he is weak in algebra.

[0148] Next, based on the knowledge graph and the user's learning history, the user's knowledge state is constructed. The present invention adopts an innovative knowledge state representation method to represent the user's knowledge state as a subgraph in the knowledge graph. Specifically, the user's knowledge state K is defined as:

[0149] K=(V K ,E K ,W K ),

[0150] Among them, V K is the set of knowledge points that the user has learned, E K is the set of relationships between these knowledge points, W K is a weight function that indicates the user’s mastery of each knowledge point. For example, W K (v) = 0.8 may indicate that the user has 80% mastery of knowledge point v.

[0151] Then, the reinforcement learning algorithm is used to find the optimal learning path in the knowledge graph. The present invention adopts an improved Q-learning algorithm to solve this problem. Define the state space S as all possible knowledge states, and the action space A as the operation of learning a new knowledge point. The Q function update rule is as follows:

[0152] Q(sa)←Q(s,a)+α[r+γmax a′ Q(s′,a′)-Q(s,a)],

[0153] Where s is the current knowledge state, a is the knowledge point selected for learning, r is the immediate reward (which can be designed based on the importance and difficulty of the knowledge point), γ is the discount factor, and α is the learning rate. The present invention recommends setting γ to 0.9 and α to 0.1, which have performed well in practice.

[0154] Finally, the optimal learning path is output as a personalized learning path. This path not only takes into account the dependencies between knowledge points, but also the user's personal situation and learning goals. For example, for a user whose goal is to master the basics of calculus, if it is found that his algebra foundation is weak, the generated learning path may first arrange some algebra review content, and then gradually introduce the concept of calculus.

[0155] The personalized learning path generation method of the present invention has significant advantages. It can not only adapt to the needs of different learners, but also dynamically adjust the learning plan to ensure the consistency and efficiency of the learning process. In addition, through the reinforcement learning algorithm, the system can continuously learn and optimize from the user's learning behavior, making the generated learning path more and more accurate and effective.

[0156] Finally, the present invention also provides an intelligent educational content management system based on knowledge graph. The system includes multiple functional modules, each of which is optimized for a specific task. Specifically, the system includes the following modules:

[0157] The data acquisition module 1 is used to acquire multimodal educational content data, including text data and audio data. This module can connect to various data sources, such as educational resource libraries, online course platforms, etc., to achieve automatic data collection and preprocessing.

[0158] Feature processing module 2, which is the core of the system, is responsible for in-depth analysis and feature extraction of educational content. Specifically, feature processing module 2 includes the following submodules:

[0159] The tensor decomposition submodule 2a is used to perform multimodal tensor decomposition and unify data of different modalities into a common feature space.

[0160] The topological analysis submodule 2b is responsible for performing topological persistent homology feature extraction to capture the structured information of educational content.

[0161] The quantum enhancer module 2c enhances feature representation by simulating quantum entanglement and captures complex nonlinear relationships.

[0162] The geometric fusion submodule 2d uses the principle of non-commutative geometry to perform feature fusion and preserve the complex relationships between features.

[0163] The knowledge extraction module 3 performs chaotic dynamics knowledge extraction based on the output of the feature processing module 2. It extracts stable knowledge structures from seemingly disordered data by analyzing the strange attractors of the chaotic system.

[0164] The knowledge graph construction module 4 is responsible for organizing the extracted knowledge into a structured knowledge graph. This module not only constructs entities and relationships, but also incorporates the deep semantic information extracted in the previous steps.

[0165] Content management module 5, which is the application layer module of the system, is responsible for performing actual educational content management tasks based on the constructed knowledge graph. It mainly includes two sub-modules:

[0166] Content retrieval submodule 5a implements intelligent content retrieval function based on knowledge graph.

[0167] The learning path generation submodule 5b is responsible for generating a personalized learning path.

[0168] This modular design makes the system highly flexible and scalable. Each module can be optimized and upgraded independently, while working together to form a powerful overall system. Through this design, the system of the present invention can effectively manage and utilize educational content and provide users with an intelligent and personalized learning experience.

[0169] In order to verify the superiority of the intelligent educational content management method and system based on knowledge graph of the present invention, a series of experiments were conducted to compare the performance of the embodiment of the present invention and two comparative examples. The following are the experimental design, results and analysis:

[0170] Embodiment 1: The complete method of the present invention is adopted, including the steps of multi-modal tensor decomposition, topological persistent homology feature extraction, quantum entanglement feature enhancement, non-commutative geometric feature fusion and chaotic dynamics knowledge extraction.

[0171] Comparative Example 1: The traditional educational content management method based on natural language processing uses only text data and adopts TF-IDF and Word2Vec for feature extraction.

[0172] Comparative Example 2: An educational content management method based on deep learning, which uses the BERT model for feature extraction but does not include multimodal data processing and advanced mathematical methods.

[0173] The following four key indicators were selected to evaluate the performance of each method:

[0174] 1. Knowledge graph completeness: measures the comprehensiveness of the constructed knowledge graph in covering educational content.

[0175] 2. Knowledge association accuracy: evaluates the accuracy of relationship recognition between knowledge points.

[0176] 3. Personalized recommendation accuracy: Test the accuracy of the learning content recommended by the system to users.

[0177] 4. Query response time: measures how quickly the system processes user queries.

[0178] The experiment was conducted on a dataset containing 10,000 educational videos and corresponding text materials. The following are the detection methods and standards for each indicator:

[0179] 1. Completeness of the knowledge graph: manually evaluated by domain experts, comparing the knowledge points in the graph with the knowledge points in the original textbooks and calculating the coverage.

[0180] 2. Knowledge association accuracy: 500 pairs of knowledge point relationships were randomly selected and their correctness was judged by experts.

[0181] 3. Personalized recommendation accuracy: 100 students were selected to compare the matching degree between the learning content recommended by the system and their actual learning needs.

[0182] 4. Query response time: record the average response time of 1,000 random queries.

[0183] The experimental results are shown in the following table:

[0184] index Example 1 Comparative Example 1 Comparative Example 2 Knowledge graph completeness 92 78 85 Knowledge association accuracy 89 72 81 Personalized recommendation accuracy 87 65 79 Query response time 0.3 seconds 0.8 seconds 0.5 seconds

[0185] It can be clearly seen from the experimental results that the method of the present invention (Example 1) is significantly better than the two comparative examples in all indicators. The specific analysis is as follows:

[0186] In terms of knowledge graph completeness, the method of the present invention reaches 92%, which is much higher than 78% of Comparative Example 1 and 85% of Comparative Example 2. This shows that the multimodal data processing and advanced mathematical methods proposed by the present invention can more comprehensively capture the knowledge points in the educational content and construct a more complete knowledge graph. In particular, the application of multimodal tensor decomposition enables the system to extract complementary information from text and audio data, greatly improving the comprehensiveness of knowledge coverage.

[0187] In terms of knowledge association accuracy, the 89% of the present invention is also significantly better than the other two methods. This is due to the application of innovative technologies such as topological continuous homology feature extraction and non-commutative geometric feature fusion. These methods can better capture the complex relationship between knowledge points, not only identifying direct associations, but also discovering implicit, multi-level knowledge structures.

[0188] The results of personalized recommendation accuracy are particularly outstanding, with the method of the present invention reaching 87%, while the traditional method is only 65%, and even the advanced deep learning method only reaches 79%. This result fully demonstrates the advantages of the present invention in generating personalized learning paths. The introduction of quantum entanglement feature enhancement and chaotic dynamics knowledge extraction enables the system to more deeply understand the learner's needs and the internal structure of knowledge, thereby providing more accurate personalized recommendations.

[0189] In terms of query response time, the method of the present invention also performs well, with an average response time of only 0.3 seconds, which is nearly 3 times faster than the traditional method and 40% faster than the deep learning method. This shows that although the present invention introduces a complex mathematical model, it actually improves the operating efficiency of the system through efficient algorithm implementation and optimization. This is of great significance for improving user experience in practical applications.

[0190] In summary, the method of the present invention is significantly superior to the existing technology in key indicators such as the completeness of the knowledge graph, the accuracy of knowledge association, the precision of personalized recommendation, and the system response speed. These advantages are mainly due to a series of innovative technologies introduced by the present invention, such as multimodal tensor decomposition, topological data analysis, quantum-inspired algorithms, etc. These methods not only improve the accuracy of knowledge representation and processing, but also enhance the system's ability to understand complex educational content.

[0191] It is particularly worth mentioning that the outstanding performance of the present invention in personalized recommendation reflects the great potential of this method in practical educational applications. Through more accurate personalized recommendations, learning efficiency can be greatly improved, which is an important step towards achieving truly personalized education.

[0192] Finally, the fast query response time shows that the present invention is not only innovative in theory, but also highly feasible in practical applications, which lays the foundation for the deployment and application of this technology in large-scale online education platforms.

[0193] In general, these experimental results strongly demonstrate the innovation and superiority of the present invention in the field of intelligent educational content management, and point out a new direction for the development of future educational technology.

[0194] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, replacement, and improvement made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent educational content management method based on knowledge graph, characterized in that: include: The acquisition steps include: Acquiring multimodal educational content data, wherein the multimodal educational content data includes text data and audio data; Processing steps include: Based on the multimodal educational content data, performing multimodal tensor decomposition to obtain a unified feature representation; According to the unified feature representation, topological continuous homology feature extraction is performed to obtain topological features; Based on the topological features, quantum entanglement feature enhancement is performed to obtain enhanced features; According to the enhanced features, performing non-commutative geometric feature fusion to obtain fused features; Based on the fusion features, chaotic dynamics knowledge extraction is performed to obtain extracted knowledge; Output steps include: The extracted knowledge is constructed into a knowledge graph, and the knowledge graph is output.

2. The method according to claim 1, characterized in that The performing of multimodal tensor decomposition specifically includes: constructing a multi-order input tensor, wherein the multi-order input tensor represents multi-modal data; Performing tensor decomposition on the multi-order input tensor to obtain a eigenvector of each mode; Based on the feature vector, the unified feature representation is generated.

3. The method according to claim 1, characterized in that The performing of topological continuous homology feature extraction specifically includes: constructing a simplicial complex based on the unified feature representation; computing the persistent homology group of the simplicial complex; generating a persistence graph, the persistence graph representing birth and death times of homology classes; The topological features of the persistence graph are analyzed to obtain the topological features.

4. The method according to claim 1, characterized in that The execution of quantum entanglement feature enhancement specifically includes: Mapping the topological features to a quantum bit space to obtain a quantum state representation; Applying quantum gate operations to the quantum state representation to create a multi-signature entangled state; Based on the multi-signature entangled state, the enhanced feature is generated.

5. The method according to claim 1, characterized in that The performing of non-commutative geometric feature fusion specifically includes: Construct noncommutative C*-algebras; defining a spectral triple, the spectral triple comprising the non-commutative C*-algebra, the Hilbert space, and the Dirac operator; Based on the spectral triples, feature fusion is performed to obtain the fused features.

6. The method according to claim 1, characterized in that The execution of chaotic dynamics knowledge extraction specifically includes: Construct feature-based chaotic mapping networks; Iteratively executing the chaotic mapping network to obtain a chaotic system; The strange attractor of the chaotic system is analyzed, and a stable knowledge structure is extracted to obtain the extracted knowledge.

7. The method according to claim 1, characterized in that Also includes: Based on the knowledge graph, perform educational content retrieval; Generate a personalized learning path based on the search results.

8. The method according to claim 7, characterized in that The performing of educational content retrieval specifically includes: Receive user query requests; Mapping the user query request to entities and relationships in the knowledge graph; Based on a graph traversal algorithm, performing multi-hop reasoning in the knowledge graph; Based on the inference results, relevant educational content is returned.

9. The method according to claim 7, characterized in that: The generating of a personalized learning path specifically includes: Obtain user learning history and learning goals; Constructing a user knowledge state based on the knowledge graph and the user learning history; Using a reinforcement learning algorithm, finding an optimal learning path in the knowledge graph; The optimal learning path is output as a personalized learning path.

10. An intelligent educational content management system based on knowledge graph that implements the method according to any one of claims 1 to 9, characterized in that: include: A data acquisition module, used to acquire multimodal educational content data, wherein the multimodal educational content data includes text data and audio data; Feature processing module for: Based on the multimodal educational content data, performing multimodal tensor decomposition to obtain a unified feature representation; According to the unified feature representation, topological continuous homology feature extraction is performed to obtain topological features; Based on the topological features, quantum entanglement feature enhancement is performed to obtain enhanced features; According to the enhanced features, performing non-commutative geometric feature fusion to obtain fused features; A knowledge extraction module, used for performing chaotic dynamics knowledge extraction based on the fusion features to obtain extracted knowledge; A knowledge graph construction module, used to construct the extracted knowledge into a knowledge graph; The content management module is used to perform educational content retrieval and personalized learning path generation based on the knowledge graph.

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