Multi-dimensional classification culture education management system based on machine learning

Abstract

The invention discloses a multi-dimensional classification culture education management system based on machine learning. Comprising a multi-factor authentication module, an intelligent examination and approval engine module, an examination and approval process customization module, a real-time examination and approval monitoring module, an examination and approval record traceability module, an automatic anomaly detection module, a dynamic authority management module, a multi-channel integration module, a high-availability design module and a data encryption and privacy protection module. All the modules cooperate with each other to form a complete cultural education management system. Automatic classification, retrieval and recommendation of cultural education resources are realized through a machine learning technology, and the management efficiency and the user experience are improved. Based on a multi-dimensional classification method, accurate classification and personalized recommendation of cultural education resources are realized, data encryption and privacy protection are enhanced, security and privacy of user data are ensured, multi-channel integration and dynamic authority management are supported, and use requirements in different scenes are met.

Description

Technical Field

[0001] The present invention relates to the technical field of cultural education management systems, and particularly to a multi-dimensional classification cultural education management system based on machine learning. Background Art

[0002] With the rapid development of information technology, the cultural education field is facing unprecedented challenges and opportunities. Traditional management methods are no longer able to meet the increasing data processing requirements and personalized learning service demands. Currently, most cultural education management systems rely on traditional database management technologies, suffering from problems such as slow data processing speed, inaccurate classification, and poor user experience. In addition, with the continuous enrichment of educational resources, how to efficiently classify, retrieve, and recommend has become an urgent problem to be solved. The successful application of machine learning technologies, especially deep learning in fields such as natural language processing and image recognition, provides the possibility for the intelligent upgrade of cultural education management systems. Therefore, this paper proposes a multi-dimensional classification cultural education management system and method based on machine learning, aiming to improve the management efficiency of cultural education resources, achieve precise teaching services, and ensure information security and privacy protection through intelligent technical means. Summary of the Invention

[0003] The present invention aims to provide a multi-dimensional classification cultural education management system and method based on machine learning. This system can automatically classify cultural education resources in multiple dimensions, achieve precise retrieval and recommendation, improve management efficiency and user experience, and at the same time ensure data security and privacy protection.

[0004] This system mainly includes a multi-factor authentication module, an intelligent approval engine module, an approval process customization module, a real-time approval monitoring module, an approval record traceability module, an automated anomaly detection module, a dynamic permission management module, a multi-channel integration module, a high-availability design module, and a data encryption and privacy protection module. These modules cooperate with each other to jointly form a complete cultural education management system.

[0005] Multi-factor Authentication Module

[0006] As the entry point of the system, this module is responsible for authenticating the user's identity. By using multiple authentication methods (such as passwords, fingerprints, facial recognition, etc.), it ensures the authenticity of the user's identity. After successful authentication, the user's request will be passed to the intelligent approval engine module for further processing.

[0007] Intelligent Approval Engine Module

[0008] The intelligent approval engine module is the core part of the system, responsible for receiving user requests and conducting risk assessment and approval policy optimization based on deep learning user behavior modeling and reinforcement learning. This module uses deep learning models such as LSTM to model user behavior, analyzing information such as users' learning habits and interest preferences; at the same time, it uses reinforcement learning algorithms such as Q learning to dynamically adjust the approval policy to cope with different approval scenarios and requirements.

[0009] Approval process customization module

[0010] According to the preset approval process configuration, this module passes requests to different approval nodes in sequence. It supports customizing the approval process to meet the needs of different scenarios. At the same time, it interacts with the real-time approval monitoring module to ensure that administrators can understand the approval progress and status in real time.

[0011] Real-time approval monitoring module

[0012] This module is responsible for monitoring the approval progress and status, and dynamically adjusting approval resources according to the actual situation. By real-time monitoring of key indicators such as the length of the approval queue and approval time, it timely discovers and solves bottleneck problems in the approval process. At the same time, it provides feedback to the approval process customization module and the intelligent approval engine module to continuously optimize the approval process.

[0013] Approval record traceability module

[0014] It records detailed information during the entire approval process, including key information such as approval time, approver, and approval result. It supports querying and tracing historical approval records to ensure the transparency and traceability of the approval process.

[0015] Automated anomaly detection module

[0016] Connected to the intelligent approval engine module and the approval process customization module, it conducts anomaly detection on approval requests. By building an anomaly detection model (such as an isolation forest-based anomaly detection algorithm), it automatically identifies and marks potential abnormal requests. When an anomaly is detected, it notifies relevant modules for further review or to take measures.

[0017] Dynamic permission management module

[0018] Dynamically adjusts user permissions based on factors such as user behavior and approval results. This module interacts with the intelligent approval engine module, the approval process customization module, and the multi-channel integration module to ensure that users' permissions are reasonable and secure across different channels. It supports fine-grained permission control policies to meet the permission management needs in different scenarios.

[0019] Multi-channel integration module

[0020] Provide users with multiple authentication and approval channels (such as Web terminals, mobile terminals, API interfaces, etc.) to ensure that users can access the system through multiple methods. At the same time, receive permission information from the dynamic permission management module to ensure permission synchronization and consistency among channels.

[0021] High-availability design module

[0022] Provide a stable operating environment for the entire system to ensure normal operation even under extreme conditions such as high concurrency and high load. Adopt technical means such as load balancing and fault tolerance mechanisms to improve the stability and reliability of the system. At the same time, support horizontal and vertical scaling to meet the needs of future business development.

[0023] Data encryption and privacy protection module

[0024] Encrypt sensitive data in the system (such as using the AES encryption algorithm) to ensure the security of data transmission and storage. At the same time, comply with relevant laws and regulations requirements and strengthen the protection of user privacy. Support flexible permission control policies and data access control policies to ensure that only authorized users can access relevant data.

[0025] The present invention also provides a multi-dimensional classification management method: The multi-dimensional classification method proposed by the present invention mainly includes three dimensions: text classification, image classification, and audio classification. By constructing a classification model based on deep learning (such as TextCNN, ResNet, CRNN, etc.), accurate classification of cultural and educational resources is achieved.

[0026] S.1 Text classification

[0027] For the text content of cultural and educational resources (such as textbooks, papers, courseware, etc.), use deep learning models such as TextCNN for text classification. This method is achieved through the following steps:

[0028] S.1.1 Data preprocessing

[0029] Word segmentation: Use a word segmentation tool (such as jieba word segmentation) to segment the text and convert the continuous text sequence into a discrete vocabulary sequence.

[0030] Stop word removal: Remove high-frequency but meaningless words in the text, such as "de", "shi", etc., to reduce the impact of noise data on model training.

[0031] Word vector conversion: Convert the segmented words into word vectors (Word Embedding). Commonly used methods include Word2Vec, GloVe, etc., to convert words into vector representations in a high-dimensional space to facilitate the model to learn the semantic relationships between words.

[0032] S.1.2 Model Construction

[0033] TextCNN Model: Construct a Convolutional Neural Network (CNN) model. Use convolutional kernels of multiple different sizes to extract local features in the text, perform feature dimensionality reduction through the pooling layer, and finally output the classification result through the fully connected layer. The TextCNN model can effectively capture n-gram features in the text and is suitable for text classification tasks.

[0034] S.1.3 Training and Evaluation

[0035] Use the labeled text dataset to train the model and optimize the model parameters through the backpropagation algorithm.

[0036] Adopt methods such as cross-validation to evaluate the model performance and select the optimal model parameters and architecture.

[0037] S.2 Image Classification

[0038] For the image content (such as illustrations, photos, etc.) in cultural and educational resources, use a Convolutional Neural Network (CNN) for image classification. The specific steps are as follows:

[0039] S.2.1 Data Preprocessing

[0040] Image Cropping and Scaling: Crop and scale images of different sizes to a unified size to meet the model input requirements.

[0041] Data Augmentation: Increase the diversity of the dataset through operations such as rotation, flipping, and scaling to improve the generalization ability of the model.

[0042] S.2.2 Model Construction

[0043] Classic CNN Models: Such as AlexNet, VGG, ResNet, etc., select a model architecture suitable for image classification tasks.

[0044] Transfer Learning: Use the model parameters pre-trained on a large dataset (such as ImageNet) as the initial values and perform fine-tuning to accelerate the training speed and improve the classification accuracy.

[0045] S.2.3 Training and Evaluation

[0046] Use the labeled image dataset to train the model and optimize the model parameters through the backpropagation algorithm.

[0047] Adopt indicators such as accuracy, recall rate, and F1 score to evaluate the model performance.

[0048] S.3 Audio Classification

[0049] For audio content in cultural and educational resources (such as voice explanations, music, etc.), a recurrent neural network (RNN) or a convolutional neural network (CNN) combined with an attention mechanism is used for audio classification. The specific steps are as follows:

[0050] S.3.1 Data preprocessing

[0051] Audio extraction and cropping: Extract audio files from videos or other media and crop them as needed.

[0052] Feature extraction: Use an audio processing library (such as librosa) to extract features such as Mel-frequency cepstral coefficients (MFCC) and short-time Fourier transform (STFT) of the audio.

[0053] S.3.2 Model construction

[0054] CRNN model: Combining the advantages of a convolutional neural network (CNN) and a recurrent neural network (RNN), first use CNN to extract local features of the audio, then use RNN to process sequence information, and finally output the classification result through a fully connected layer.

[0055] Attention mechanism: Add an attention mechanism to the RNN layer or the fully connected layer to enable the model to focus on important parts of the audio and improve the classification accuracy.

[0056] S.3.3 Training and evaluation

[0057] Use the labeled audio dataset to train the model and optimize the model parameters through the backpropagation algorithm.

[0058] Adopt indicators such as accuracy and confusion matrix to evaluate the model performance.

[0059] The present invention has the following beneficial effects: realizing the automatic classification, retrieval and recommendation of cultural and educational resources through machine learning technology, improving the management efficiency and user experience. Based on a multi-dimensional classification method, realizing the accurate classification and personalized recommendation of cultural and educational resources, strengthening data encryption and privacy protection, ensuring the security and privacy of user data, and supporting multi-channel integration and dynamic permission management to meet the usage requirements in different scenarios. Description of the drawings

[0060] Figure 1 System principle flowchart; Specific implementation manners

[0061] The following further describes the implementation manners of the present invention in detail in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0062] Example 1

[0063] I. System Embodiment 1. Multi-Factor Authentication Module: In addition to common authentication methods such as passwords, fingerprints, and facial recognition, behavioral biometric authentication is introduced. For example, authentication is performed by analyzing features such as the keystroke rhythm, force, and interval time when the user enters the password. Parameter definitions include the average keystroke time interval threshold, force change range, etc. When the keystroke features of the user deviate from the pre-stored features within a certain range, the authentication is passed. The blockchain-based authentication technology is adopted to store the user's authentication information on the blockchain to ensure the immutability and security of the authentication information. Parameters of the blockchain such as the block size and the difficulty coefficient of the consensus algorithm need to be adjusted according to the system's performance and security requirements. 2. Intelligent Approval Engine Module: In addition to the LSTM and Q learning algorithms, the DDPG (Deep Deterministic Policy Gradient) algorithm in deep reinforcement learning is introduced for user behavior modeling and approval policy optimization. The DDPG algorithm combines the perception ability of deep learning and the decision-making ability of reinforcement learning, and can better adapt to complex approval scenarios. Parameter definitions include the number of layers, the number of neurons, and the learning rate of the actor network and the critic network. The fuzzy logic system is used to handle the uncertainty of user behavior. Fuzzy sets such as "high-risk behavior", "medium-risk behavior", "low-risk behavior", etc. are defined, and the fuzzy set to which the user belongs is determined according to the user's behavior characteristics. Parameters of the fuzzy rules such as the shape of the membership function and the overlapping degree between fuzzy sets can be adjusted according to the actual situation. 3. Approval Process Customization Module: The genetic algorithm is introduced to optimize the approval process. The approval process is represented as a chromosome, and the optimal approval process configuration is found through genetic operations such as selection, crossover, and mutation. Parameters of the genetic algorithm include the population size, crossover probability, mutation probability, etc. The complexity index of the approval process is defined, such as the number of approval nodes and the length of the approval path. The complexity of the approval process is minimized through the optimization algorithm to improve the approval efficiency. 4. Real-Time Approval Monitoring Module: The chaos theory is used to analyze the dynamic changes of the approval queue. Parameters of the chaos system such as the control parameter and the initial condition are defined, and the characteristic index of the chaos system such as the Lyapunov exponent is calculated to determine whether the approval queue is in a chaotic state. When chaotic characteristics appear in the approval queue, corresponding measures such as adjusting the approval resource allocation and optimizing the approval process are taken. The multi-agent system is introduced to dynamically allocate approval resources. The approval nodes are regarded as agents, and the optimal allocation of approval resources is achieved through the interaction and coordination between agents. Parameters of the multi-agent system such as the number of agents, the communication radius, and the decision rule need to be adjusted according to the scale and characteristics of the approval system. 5. Approval Record Traceability Module: The blockchain technology is adopted to ensure the immutability and traceability of the approval records. The structure of the blockchain such as the block size and the length of the chain is defined, as well as the parameters of the consensus algorithm such as the difficulty coefficient of the proof of work.Introduce digital signature technology to sign approval records to ensure the authenticity and integrity of the records. Parameters of digital signatures such as key length and strength of the signature algorithm need to be selected according to security requirements. 6. Automated anomaly detection module: In addition to the anomaly detection algorithm based on isolation forest, introduce anomaly detection methods based on deep learning, such as Autoencoder. Autoencoder can detect anomaly data that is significantly different from normal data by learning the feature representation of normal data. Parameters of Autoencoder include the number of network layers, the number of neurons, activation functions, etc. Define the threshold for anomaly detection. When the anomaly score exceeds the threshold, it is marked as an anomaly request. The determination of the threshold can be done through statistical analysis or empirical rules and adjusted according to the actual situation. 7. Dynamic permission management module: Adopt a combination of role-based access control (RBAC) and attribute-based access control (ABAC) for permission management. Define the set of roles and attributes, as well as the rules for permission allocation. Parameters such as the number of roles, the dimension of attributes, and the complexity of rules need to be adjusted according to the system's requirements. Introduce a trust evaluation model to evaluate the trustworthiness of users and dynamically adjust users' permissions according to the trustworthiness. Parameters of the trust evaluation model such as the calculation method of trustworthiness and the classification of trustworthiness levels can be defined according to the actual situation. 8. Multi-channel integration module: Implement multi-channel integration using a microservices architecture. Split the functions of the system into multiple independent microservices, and each microservice is responsible for a specific function, such as authentication service, approval service, resource management service, etc. Parameters of the microservices architecture such as the number of services, the deployment method of services, and communication protocols need to be adjusted according to the scale and performance requirements of the system. Introduce an API gateway to uniformly manage multi-channel APIs. Define parameters of the API gateway such as routing rules, load balancing strategies, and security policies to ensure the safe and efficient invocation of APIs for each channel. 9. High-availability design module: Implement the high availability of the system using distributed consensus algorithms such as Paxos or Raft. Define parameters of the consensus algorithm such as the number of nodes, election timeout, and log replication strategy to ensure that the system can quickly recover in case of node failures. Introduce fault injection technology to test the high availability of the system. Define parameters of fault injection such as fault type, fault occurrence frequency, and fault impact range, and verify the reliability and fault tolerance of the system by simulating various fault situations. 10. Data encryption and privacy protection module: Use homomorphic encryption technology to encrypt sensitive data. Homomorphic encryption allows specific computational operations to be performed on encrypted data without decrypting the data, thereby protecting the privacy of the data. Parameters of homomorphic encryption such as the type of encryption algorithm, key length, and computational complexity need to be selected according to the performance and security requirements of the system. Introduce differential privacy technology for data privacy protection. Define parameters of differential privacy such as privacy budget and noise addition mechanism to ensure that users' privacy information is not leaked during data publication and analysis.II. Multi-dimensional Classification Method - Example 1 1. Text Classification: - Data Preprocessing: In addition to the jieba word segmentation tool, introduce a deep learning-based word segmentation method such as Byte Pair Encoding (BPE). BPE automatically generates a vocabulary by learning the statistical patterns of character sequences and can better handle out-of-vocabulary words and new words. Parameters of BPE such as the vocabulary size and the number of merges can be adjusted according to the characteristics of the text data. Define complexity metrics for the text, such as lexical richness and sentence length distribution. Screen and preprocess the text according to the complexity metrics to remove overly simple or complex text and improve the training effect of the model. - Model Construction: In addition to the TextCNN model, introduce the Transformer model for text classification. The Transformer model uses the self-attention mechanism to model the global semantic relationships of the text and can better capture the context information of the text. Parameters of the Transformer model such as the number of layers, the number of heads, and the hidden layer dimension need to be adjusted according to the difficulty of the text classification task and the data scale. Adopt an ensemble learning method to combine multiple different text classification models. Define ensemble learning strategies such as voting method and weighted average method, as well as the model selection and weight assignment rules. - Training and Evaluation: Introduce adversarial training techniques to improve the robustness of the model. During the training process, generate adversarial samples to attack the model and optimize the model parameters so that the model can resist adversarial attacks. Parameters of adversarial training such as the generation method of adversarial samples and the attack intensity need to be adjusted according to the performance and security requirements of the model. Define the weights of evaluation metrics. Metrics such as accuracy, recall, and F1 value can be weighted and summed according to the needs of the actual application scenario to obtain a comprehensive evaluation result. 2. Image Classification: - Data Preprocessing: In addition to traditional data augmentation methods, introduce Generative Adversarial Networks (GANs) for data augmentation. GANs can generate new images similar to real images and increase the diversity of the dataset. Parameters of GANs such as the structures of the generator and discriminator, the number of training epochs, and the learning rate need to be adjusted according to the characteristics of the image data. Define the dimension compression method for the feature vectors of the images, such as Principal Component Analysis (PCA) or t-SNE dimensionality reduction. By reducing the dimension of the feature vectors, the computational amount is reduced and the training efficiency of the model is improved. - Model Construction: In addition to the classic CNN model, introduce Capsule Networks (CapsNet) for image classification. CapsNet can better handle the spatial relationships and rotational invariance of images. Parameters of CapsNet such as the number of capsules, the dimension, and the parameters of the routing algorithm need to be adjusted according to the requirements of the image classification task. Adopt model compression techniques such as pruning and quantization to compress the image classification model. Define parameters such as the compression ratio and the tolerance of precision loss to reduce the size and computational amount of the model while ensuring the performance of the model. - Training and Evaluation: Introduce knowledge distillation technology to transfer the knowledge of a complex teacher model to a simple student model.Define the structures of the teacher model and the student model, as well as the loss function for knowledge distillation. Optimize the parameters of the student model so that the student model can approach the performance of the teacher model. Adopt a multi-modal fusion method to fuse different features of the image. For example, fuse the color features, texture features, and shape features of the image to improve the accuracy of image classification. Define the fusion strategy and parameters, such as the weight assignment of features and the fusion method. 3. Audio classification: Data preprocessing: In addition to the librosa library, introduce other audio processing tools such as Aubio or Essentia. These tools can provide more audio feature extraction methods and functions. Define the parameters of different tools, such as the sampling rate, window size, and frame shift, and select and adjust them according to the characteristics of the audio data. Adopt time-frequency analysis methods for audio signals, such as wavelet transform or Hilbert-Huang transform (HHT). These methods can provide richer audio features and improve the accuracy of audio classification. Define the parameters of the time-frequency analysis method, such as the selection of wavelet basis functions and the decomposition level of HHT. Model construction: In addition to the CRNN model, introduce a graph neural network (GNN) for audio classification. The GNN can represent the audio signal as a graph structure and better handle the time series relationship and spectral structure of the audio signal. The parameters of the GNN, such as the graph construction method, the feature representation of nodes and edges, and the parameters of the training algorithm, need to be adjusted according to the characteristics of the audio data. Adopt variants of the attention mechanism, such as multi-head attention mechanism or global attention mechanism. Define the parameters of the attention mechanism, such as the number of heads and the calculation method of attention weights, to improve the model's attention to important parts in the audio. Training and evaluation: Introduce semi-supervised learning techniques for system implementation using a small amount of labeled data.

[0064] Data acquisition module

[0065] Data source: Relevant data on culture and education can be collected from channels such as the school's teaching management system, student information system, and curriculum management system. In addition, relevant data resources can also be obtained from the Internet through web crawler technology.

[0066] Data format: Support the acquisition of multiple data formats, including structured data (such as tabular data in a database), semi-structured data (such as XML and JSON format data), and unstructured data (such as text, pictures, audio, and video).

[0067] Data preprocessing module

[0068] Data cleaning: Remove invalid data and noise data, such as duplicate data, error data, missing data, etc. Data deduplication, data verification, data filling, etc. can be used for cleaning.

[0069] Data denoising: Adopt techniques such as filtering and smoothing to remove noise in the data and improve the data quality.

[0070] Data normalization: Normalize the data so that data in different dimensions has the same scale and range, facilitating subsequent processing by machine learning algorithms.

[0071] Multi-dimensional classification module

[0072] Classification criteria: Determine different classification criteria according to the needs of cultural and educational management. For example, classification can be carried out according to students' age, gender, academic performance, hobbies, etc.; according to the type, difficulty, duration, teaching method, etc. of courses; according to the type, format, use, source, etc. of teaching resources.

[0073] Classification algorithm: Use classification algorithms in machine learning algorithms, such as decision trees, support vector machines, naive Bayes, etc., to perform multi-dimensional classification on the preprocessed data. During the classification process, appropriate algorithms and parameters can be selected according to the actual situation to improve the accuracy and efficiency of classification.

[0074] Machine learning module

[0075] Model selection: Select a suitable machine learning model according to the task requirements of cultural and educational management. For example, for classification tasks, classification models such as decision trees, support vector machines, naive Bayes, etc. can be selected; for prediction tasks, prediction models such as linear regression, logistic regression, neural networks, etc. can be selected.

[0076] Model training: Use the collected cultural and educational data to train the selected machine learning model. During the training process, methods such as batch training and incremental training can be adopted to improve the training efficiency and the adaptability of the model.

[0077] Model optimization: Adopt techniques such as cross-validation, regularization, and ensemble learning to optimize the trained model and improve the generalization ability and accuracy of the model.

[0078] Management decision-making module

[0079] Decision-making: Make cultural and educational management decisions based on the model established by the machine learning module. For example, recommend suitable courses and teaching resources according to the type and needs of students; reasonably arrange the teaching progress and teaching methods according to the type and difficulty of courses; optimize the allocation and management of teaching resources according to the usage of teaching resources.

[0080] Decision evaluation: Evaluate and feedback the made management decisions, and adjust and optimize the decisions in a timely manner to improve the scientificity and effectiveness of management decisions.

[0081] User interaction module

[0082] Interface Design: Design a user - friendly interface for convenient querying, analysis, and management of cultural and educational data. The interface should be simple, clear, and easy to operate.

[0083] Function Implementation: Implement functions such as user query, data analysis, and management decision adjustment. Users can view information such as students' learning situations, course arrangements, and teaching resource usage through the interface, and can also adjust and optimize management decisions.

[0084] Classify audio with a large amount of unlabeled data. Define semi - supervised learning algorithms such as self - training and co - training, as well as parameters such as the ratio of labeled data to unlabeled data and the number of iterations. Adopt a dynamic adjustment method for audio classification evaluation metrics such as accuracy, recall rate, and F1 - value. According to different audio categories and application scenarios, adjust the weights and thresholds of evaluation metrics to better reflect the performance of the model.

[0085] Example Three

[0086] A multi - dimensional classification cultural and educational management system based on machine learning, including:

[0087] Data Acquisition Module: Used to collect cultural and educational related data, including student information, course information, teaching resource information, exam score information, etc. The data acquisition channels are extensive and can obtain relevant data from school management systems, online learning platforms, educational databases, and through web crawlers from the Internet. Support multiple data formats, such as structured data (database tables), semi - structured data (XML, JSON), and unstructured data (text, pictures, audio, video).

[0088] Data Pre - processing Module: Perform pre - processing operations such as data cleaning, denoising, and normalization on the collected data to improve the quality and usability of the data.

[0089] Data Cleaning: Remove invalid data (duplicate data, error data, missing data, etc.), and methods such as data deduplication, data verification, and data filling can be used.

[0090] Data Denoising: Adopt techniques such as filtering and smoothing to remove noise in the data and improve data quality. For example, use the median filtering algorithm to denoise image data.

[0091] Data Normalization: Normalize the data so that data in different dimensions have the same scale and range, facilitating subsequent machine learning algorithm processing. Normalization methods can use min - max normalization, Z - score standardization, etc.

[0092] Multi - dimensional Classification Module: According to different classification criteria, perform multi - dimensional classification on the pre - processed data, including student type classification, course type classification, teaching resource type classification, etc.

[0093] Classification criteria: Different classification criteria are determined according to the needs of cultural and educational management. For example, classification can be carried out according to students' age, gender, academic performance, interests and hobbies, learning styles, etc.; according to the type, difficulty, duration, teaching method, teaching objectives, etc. of the courses; according to the type, format, use, source, quality, etc. of teaching resources.

[0094] Classification algorithms: Classification algorithms in machine learning algorithms and unconventional algorithms are used for multi-dimensional classification. Conventional classification algorithms such as decision trees, support vector machines, naive Bayes, etc.; unconventional algorithms include fuzzy logic algorithms, chaos theory models, etc.

[0095] Fuzzy logic algorithm: Data is fuzzily classified by defining fuzzy sets and fuzzy rules. For example, for students' academic performance, fuzzy sets such as "excellent", "good", "medium", "poor", etc. can be defined, and the fuzzy set to which a student belongs can be determined according to the specific score value of the student. The fuzzy logic algorithm can handle uncertain and fuzzy data, improving the accuracy and adaptability of classification.

[0096] Chaos theory model: The chaos theory is used to analyze and classify data. Chaotic systems have characteristics such as sensitivity to initial conditions, ergodicity, and randomness, and can be used to discover complex patterns and rules in data. For example, for students' learning behavior data, the chaos theory model can be used to analyze its time series characteristics, thereby classifying students' learning status.

[0097] Machine learning module: Machine learning algorithms are used to train and optimize the multi-dimensionally classified data, and classification models and prediction models are established.

[0098] Model selection: According to the task requirements of cultural and educational management, appropriate machine learning models are selected. For example, for classification tasks, classification models such as decision trees, support vector machines, naive Bayes, etc. can be selected; for prediction tasks, prediction models such as linear regression, logistic regression, neural networks, etc. can be selected. At the same time, by combining unconventional algorithms and mathematical models, such as fuzzy neural networks, chaos neural networks, etc., the performance and adaptability of the model are improved.

[0099] Model training: The selected machine learning model is trained using the collected cultural and educational data. During the training process, methods such as batch training and incremental training can be adopted to improve the training efficiency and the adaptability of the model.

[0100] Model Optimization: Techniques such as cross-validation, regularization, and ensemble learning are used to optimize the trained model, improving the model's generalization ability and accuracy. At the same time, in combination with the characteristics of unconventional algorithms and mathematical models, targeted optimization is carried out. For example, for fuzzy neural networks, methods such as fuzzy rule optimization and membership function adjustment can be used for optimization; for chaotic neural networks, methods such as chaotic parameter adjustment and chaotic initialization can be used for optimization.

[0101] Management Decision-making Module: Based on the model established by the machine learning module, cultural and educational management decisions are made, including course arrangement, teaching resource allocation, and personalized education for students.

[0102] Decision-making: Based on the output results of the machine learning model, cultural and educational management decisions are made. For example, according to the types and needs of students, suitable courses and teaching resources are recommended for them; according to the types and difficulties of courses, the teaching progress and teaching methods are reasonably arranged; according to the usage of teaching resources, the allocation and management of teaching resources are optimized.

[0103] Decision Evaluation: The formulated management decisions are evaluated and feedback is provided, and the decisions are adjusted and optimized in a timely manner to improve the scientificity and effectiveness of the management decisions. Evaluation metrics such as accuracy, recall rate, and F1 value can be used to quantitatively evaluate the decisions.

[0104] User Interaction Module: A friendly interaction interface is provided for users to facilitate querying, analyzing, and managing cultural and educational data.

[0105] Interface Design: A friendly user interaction interface is designed to facilitate users to query, analyze, and manage cultural and educational data. The interface should be simple, clear, and easy to operate. Graphical interfaces, visual charts, etc. can be used to display data and decision results.

[0106] Function Implementation: Functions such as user query, data analysis, and management decision adjustment are implemented. Users can view information such as students' learning situations, course arrangements, and teaching resource usage through the interface, and can also adjust and optimize management decisions.

[0107] A multi-dimensional classification cultural and educational management method based on machine learning includes the following steps:

[0108] Data Collection: Relevant cultural and educational data is collected through various channels and stored in a database.

[0109] Data Preprocessing: Preprocessing operations such as cleaning, denoising, and normalization are performed on the collected data to remove invalid data and noise data, improving the quality and usability of the data.

[0110] Multi-dimensional Classification: According to different classification criteria, the preprocessed data is classified multi-dimensionally.

[0111] Determine the classification criteria and methods, and classify according to the needs of cultural and educational management.

[0112] Use conventional classification algorithms and unconventional algorithms to classify data multi-dimensionally, and obtain classification results in different dimensions.

[0113] Machine learning model training and optimization: Use machine learning algorithms to train and optimize the multi-dimensionally classified data, and establish classification models and prediction models.

[0114] Select a suitable combination of machine learning models, unconventional algorithms, and mathematical models.

[0115] Conduct model training, and adopt methods such as batch training and incremental training to improve training efficiency.

[0116] Adopt techniques such as cross-validation, regularization, and ensemble learning, and combine with the characteristics of unconventional algorithms to optimize the model.

[0117] Management decision-making: According to the models established by the machine learning module, make cultural and educational management decisions.

[0118] Make management decisions according to the model output results, such as course arrangement, teaching resource allocation, and personalized education for students.

[0119] Evaluate and feedback on management decisions, and adjust and optimize decisions in a timely manner.

[0120] User interaction: Provide a friendly interaction interface for users to facilitate querying, analyzing, and managing cultural and educational data.

[0121] Design a user interaction interface to display data and decision results.

[0122] Implement functions such as user query, data analysis, and management decision adjustment.

[0123] (III) Beneficial effects

[0124] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0125] Improve the efficiency and accuracy of cultural and educational management. Through the combination of machine learning algorithms and unconventional algorithms, data is automatically classified and analyzed, reducing manual intervention and improving management efficiency and accuracy.

[0126] Realize multi-dimensional classification. The present invention can classify cultural and educational data multi-dimensionally according to different classification criteria, comprehensively reflect the characteristics and laws of the data, and provide richer information for management decisions. Especially after introducing unconventional algorithms, complex patterns and laws in the data can be discovered, improving the accuracy and adaptability of classification.

[0127] Personalized education services are provided. Suitable courses and teaching resources are recommended according to the types and needs of students, realizing personalized education and improving the learning effects and satisfaction of students.

[0128] The allocation and management of teaching resources are optimized. By analyzing the usage of teaching resources, teaching resources are reasonably allocated and managed, improving the utilization rate and benefits of teaching resources.

[0129] The adaptability and robustness of the system are enhanced. The introduction of unconventional algorithms and mathematical models enables the system to better handle complex and variable cultural education data, improving the adaptability and robustness of the system.

[0130] The embodiments of the present invention are given for purposes of illustration and description, and are not exhaustive or limit the invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are chosen and described in order to better explain the principles of the invention and its practical applications, and to enable those of ordinary skill in the art to understand the invention and design various embodiments with various modifications suitable for specific purposes.

Claims

1. A multi-dimensional classification cultural education management system based on machine learning, characterized in that: It includes multi-factor authentication module, intelligent approval engine module, approval process customization module, real-time approval monitoring module, approval record traceability module, automated anomaly detection module, dynamic permission management module, multi-channel integration module, high availability design module and data encryption and privacy protection module. The modules work together to form a complete cultural education management system.

2. The multi-dimensional classification cultural education management system as claimed in claim 1, characterized in that: The multi-factor authentication module is used to authenticate the user's identity, using multiple authentication methods to ensure the authenticity of the user's identity, and after the authentication is passed, the user's request is passed to the intelligent approval engine module.

3. The multi-dimensional classification cultural education management system as claimed in claim 1, characterized in that: The intelligent approval engine module, as the core part of the system, receives user requests and performs user behavior modeling based on deep learning and risk assessment and approval strategy optimization based on reinforcement learning. It uses deep learning models such as LSTM to model user behavior and adopts Qlearning reinforcement learning algorithm to dynamically adjust the approval strategy.

4. The multi-dimensional classification cultural education management system according to claim 1, characterized in that: The approval process customization module passes the request to different approval nodes in sequence according to the preset approval process configuration, supports customized approval process, and interacts with the real-time approval monitoring module to ensure that the administrator understands the approval progress and status in real time.

5. The multi-dimensional classification cultural education management system according to claim 1, characterized in that: The real-time approval monitoring module is responsible for monitoring the approval progress and status, dynamically adjusting approval resources according to actual conditions, timely discovering and solving bottleneck problems in the approval process through real-time monitoring of key indicators, and providing feedback to the approval process customization module and the intelligent approval engine module to optimize the approval process.

6. The multi-dimensional classification cultural education management system according to claim 1, characterized in that: The approval record traceability module records detailed information in the approval process, including key information such as approval time, approver, and approval result, and supports query and tracing of historical approval records to ensure the transparency and traceability of the approval process.

7. The multi-dimensional classification cultural education management system according to claim 1, characterized in that: The automated anomaly detection module is connected to the intelligent approval engine module and the approval process customization module to perform anomaly detection on approval requests. It automatically identifies and marks potential abnormal requests by building an anomaly detection model, and notifies relevant modules for further review or measures when anomalies are found.

8. The multi-dimensional classification cultural education management system according to claim 1, characterized in that: The dynamic permission management module dynamically adjusts user permissions based on factors such as user behavior and approval results, interacts with the intelligent approval engine module, the approval process customization module, and the multi-channel integration module, and supports fine-grained permission control strategies to meet permission management needs in different scenarios.

9. The multi-dimensional classification cultural education management system according to claim 1, characterized in that: The multi-channel integration module provides users with multiple authentication and approval channels, ensuring that users can access the system in multiple ways and receive permission information from the dynamic permission management module to ensure synchronization and consistency of permissions among channels.

10. The multi-dimensional classification cultural education management system according to claim 1, characterized in that: The high availability design module provides a stable operating environment for the entire system, adopts load balancing, fault tolerance mechanism and other technical means to improve the stability and reliability of the system, and supports horizontal expansion and vertical expansion to meet the needs of future business development; at the same time, the data encryption and privacy protection module encrypts sensitive data in the system, complies with relevant laws and regulations to strengthen user privacy protection, and supports flexible permission control strategies and data access control strategies to ensure that only authorized users can access relevant data.

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