With the establishment of emerging fields, such as big data, cloud computing, and mobile Internet, and the increase in people’s demand for education, combined with the epidemic environmental factors, the online education model of ‘Internet + education’ has become popular throughout the country [1, 2]. However, the vast amount of learning resources on the Internet makes users hesitate to make decisions in the process of acquiring learning materials suitable for them, which may even lead to ‘information trek’, ‘cognitive overload’ and other troubles [3–6]. Therefore, it is an urgent to construct a special and efficient educational resource recommendation system based on the user’s resource preference portrait of the computer system. At present, most of the recommendation systems have a relatively simple and rough recommendation model with a single computing architecture, and the user response to the recommendation results is not ideal. In recent years, a lot of research has been focussed on user group portrait features and in-depth positioning of user group preference similarity to identify the resource needs of large groups and to meet the needs of a vast number of users [7]. However, this situation only focuses on user information collection and does not involve the mining and analysis of educational resources, let alone the optimisation and filtering of the quality of educational resources. In order to adapt the user’s specific preferences to the resource information corresponding to the high quality, this study will use MRLG Rec to optimise the current educational resource recommendation system [8–10].

Reinforcement learning (RL) is a research hotspot of system class algorithm integration. The basic principle of RL is to obtain the optimal strategy as the learning objective by requiring the agent to obtain the maximum cumulative reward from the state analysis of the environment [11–13]. To sum up, RL attaches great importance to the strategy optimisation of learning problem-solving. Generative adversarial networks (GANs) are basic carriers of generative models through adversarial estimation. With the continuous expansion of Internet information technology, the emergence of GANs can stimulate the enthusiasm of research in many computing fields. The deep intersection of RL and GANs is synthesised, and the reward mechanism of RL and strategy gradient technology are used to assist GANs in the multilayer neural network and nonlinear transformation, and the bottom layer features are integrated to form the representation category and feature feedback with high perception and strong computational capacity, and the new algorithm mode of MRLG Rec is obtained [14–17]. Accordingly, a preliminary educational resource recommendation system framework based on MRLG Rec is obtained, as shown in Figure 1.

To this end, in this paper, model-based RL with generative adversarial networks and attention mechanism are introduced for recommendation. MRLG Rec conducts research on the educational resource recommendation system [17–21]. MRLG Rec is used to calculate the similar association between users and educational resources, and to establish a deep filtering and re-matching mechanism between users and the recommendation system according to their search history and preference behaviour so as to eliminate the problem of information overload [22–25].

Reinforcement learning is one of the main methods to solve most system algorithms, which can carry out iterative analysis and make exploratory evaluation of problems. Among them, the Markov decision process (MDP) can match the theoretical level of RL to create modular questions. The MDP is generally defined as A quad, that is, M = < S, A, R, P >, where S is the set of states, A is the action set, R is the reward function, R (s, a) refers to the reward value obtained by taking action A in state S and P is the state transition function, P (s, a, s′) is the probability of states transferring from a serialised action a to state s′. The root of RL is the strategy of maximising cumulative rewards through calculating learning degrees, which can be used by system core analysis to make subsequent decisions.

_{K}, strategy as h, artificial intelligence according to s_{k} and strategy h select action a_{K} and immediate reward R (s_{k}, a_{k}), and the system will record this state as s_{K} +1. The operation theory of RL can be simplified the system shown in Figure 2.

By above can lenovo education resource recommendation system based on RL is a one-way design: the initial state is equivalent to the learner (user) requirements, through RL deep cycle probability calculation and recording of MDP model, can be the user’s resources on keywords of radiation spread (action), leads to the results of multiple resources feedback. Then, according to the advantages and disadvantages of multiple resource results, the optimal result is obtained so as to meet the resource response of user groups. The results obtained in the circular radiation distribution of RL can be used as log records to integrate a new database to record the source point of teaching resources and simplify the generation time of subsequential repeated actions.

The GAN is an element calculation problem dealing with minimal and maximal boundaries. Its target value can be expressed numerically by the following Eq. (1):

To explore the distribution p data of real data X, we can first set the noise variable p_{z} (z) and derive the mapping G(z; _{g}_{g}_{d}

The continuous interaction between real data and the generated model, the internalisation of the resulting data records and the filtering, and the iterative process can continuously improve the performance of the generator model. In view of this, RL and GAN jointly form the recommendation algorithm of RL and GAN (MRLG Rec), and the running process is shown in Figure 4.

As can be seen from Figure 4, the operation time required by the parallel operation of GAN and RL agent will be lengthened to some extent. However, the lengthening of time dimension can undoubtedly promote the organic learning of system self-consciousness. Similarly, incorporating the MRLG Rec algorithm into the educational resource recommendation system on the basis of the previous section will have more advantages than the single RL algorithm.

The single educational recommendation system operates as follows: users are classified in a centralised way, and then the corresponding cognitive similarity is calculated in the group characteristics of users to identify ‘neighbour users’, and the system carries out corresponding content push according to this label. It mainly contains the following aspects of the concept of edge.

User base: The user database is used to store and write the characteristic information of the user group, including the nominal personality and behaviour personality. These characteristics are inherent to user groups and cannot be easily changed or altered. Personalised information includes but not limited to age, occupation, residential address and other early identification information used to confirm the user’s portrait. This information can be defined as static and does not change frequently. On the other hand, behavioural information is dynamic information, including user login address, click number and comment behaviour. Both static information and dynamic information are important elements of the educational resource recommendation system.

Repository: The resource library covers educational materials, learning materials and markers. In this database, theorems and algorithms in each chapter are regarded as branches of the knowledge tree. Learning resources can be derived from the branch concept nouns of the knowledge tree to match the corresponding resource information in the network space. As long as the source of the branch concept is searched and captured on the network, the teaching resource can be obtained.

User analysis: Combined with the collection recognition of user characteristic information in the data layer, the single educational resource recommendation system analyses the resource preference behaviour of user groups, then describes the user group portrait and classifies the group image. Therefore, this analysis is the basis for the educational resource recommendation system. Multiple users or user groups with high correlation will be designated as ‘neighbourhood’. On the basis of ‘neighbourhood’ users, relevant resources can be matched according to the correlation between current users and ‘neighbourhood’ users so as to achieve accurate and simple recommendation effect.

Resource analysis: Resource analysis integrates factors such as tags, clicks and resource comments as attributes of learning materials. The system builds a special database for the attributes and characteristics of learning materials and makes statistical modelling of this kind of information set so as to evaluate the similarity and quality of learning materials. The similarity analysis between learning materials is based on the modelling of learning resource recommendation. Echoing the concept mentioned in the user analysis, similar data can be obtained as ‘neighbourhood’ data. Once the system algorithm detects that a user is searching for similar data, the ‘neighbourhood’ data can be recommended to the current user. The quality analysis of learning materials is based on the evaluation of attributes such as post-use comments and usage of user data so that inferior educational resource content can be filtered accordingly.

According to the overlapping information collection of data layer and data analysis layer, a search recommendation mechanism can be formed by initiating search from user preferences, behaviour data and data resource collection radiation. In the above combination of forms, the multiple recommendation mechanism of the single educational recommendation system without strengthening the selection algorithm is shown in Figure 5.

In order to measure the advantages and disadvantages of MRLG Rec educational resource recommendation strategy, the concept of state value function is introduced into the reinforcement algorithm, and the value function is used to evaluate the strategy. Specifically, the value function is divided into state value function V^{h}^{h}^{h}^{h}^{h}^{h}

Here, γ is the discount factor. The optimal strategy h * refers to the strategy that can obtain the maximum cumulative reward, and its corresponding optimal value function

The purpose of the GAN is to improve the generative capability of the generator model G and discriminant model D so as to achieve the Nash equilibrium. Definition of intensive study in the process of learning sample set based on real experience (s, a), (after the state s′, r) reward come in pairs, on a moment s corresponding to the corresponding state of action a, (s, a) is known as the state of dynamic, migration to the state s′ of the next moment, and get an immediate reward r, (s′, r) is referred to as a follow-up state reward pair. Therefore,

Here, _{1} represents the state action pair and _{2} represents the continued state reward pair. Since the subsequent state s′ and reward r are based on the state S at the previous moment and the corresponding action A, there is a definite connection between _{1} and _{2}, and mutual information I is used to represent the association between the two:

Here, _{2}) represents the entropy of _{2} and is used to measure the uncertainty of _{2}. _{2} | _{1} is the uncertainty of _{2} given _{1}. _{2}._{1}) shows the reduction in the uncertainty of _{2} caused by _{1}. Since _{1} and _{2} are correlated, mutual information _{1}, and the output is _{2}. This relational correction unit is used to train the internal connection between _{1} and _{2} in the experience sample set.

Consistent with the experience sample set, the experience sample

Here, _{1}(_{2}(_{2}(_{1}(_{1}, _{2}]. Therefore, by combining the generated sample _{1}(_{1}(_{2}(_{2}(_{2}(_{2}(_{2}(

Definition = _{1} is the similarity accuracy of MRLG Rec educational resource recommendation system.

In order to measure the advantages and disadvantages of a single educational resource recommendation strategy, the behaviour sequence of users is defined as a finite set

Here, (_{i},_{i}) represents the i-th element pair, _{i}_{i},_{i}) composed of elements in the element pair (_{i},_{i}) is represented by Si, which is called the i-th state string of the user.

A sequence of states is a string of successively linked elements in each pair of elements in a sequence of actions. For example, the state sequence of the user S can be expressed as ‘_{1},_{2}…_{n}.

The state subsequence of state sequence S is defined as follows:

Let the state sequence of user

Among them,

Represents state value similarity, state transition similarity and state order similarity respectively.

Based on the three similarity values, the learning behaviour of users in different time periods has different contributions to the prediction of their learning behaviour. Generally speaking, the closer the behaviour, the more it reflects the user’s interest in learning and contributes more to the similarity between users. In order to improve the importance of recent behaviour sequences to similarity calculation, time weight function WT was introduced.

Here, SA is the set of all the behaviour sequences of user A, and DA and Si represent the time interval between the behaviour sequence generated by user A and a behaviour sequence generated at the earliest. LA represents the time span of user A’s behaviour sequence and

When analysing relationships between users, it is not enough to consider only behavioural similarity. There are many reasons for the high degree of similarity, such as the inability to observe long-term differences in user behaviour over a short period of time. However, in practical application, a more accurate and stable description of the relationship between users is needed. Therefore, this article proposes the concept of correlation coefficient, that is, the similarity between users in a certain period of time can be obtained by analysing the change of similarity. Assuming that the average similarity is _{avg} and the variance is sim_{dx}, the correlation coefficient (RC) can be calculated by using the following equation:

Therefore, the closer the relationship between two users, the greater the change of the average similarity. On the contrary, the change of average similarity is smaller.

Definition similarity accuracy of _{2} as a single educational resource recommendation system

Since energy consumption data are a time-series, the prediction model sample is also time-series structure. According to the previous analysis, the prediction model sample includes the following parts:

_{1},_{2},…,_{i}),(_{2},_{3},…,_{i+1}),…,(_{2},_{3},…,_{i}+1)}, It is called the training sample set, which contains k energy consumption data of the first

_{1},_{2},…,_{k}} It is called the action space set, and the action size ranges from [_{min}, _{max}] divided by interval M, which is variable.

_{1},_{2},…,_{k}}, _{k} = −_{k} − _{k+i}

It is called the reward sample set, and the reward value is the negative value of the absolute value of the difference between the action value taken in each state and the real value of the energy consumption at the next moment. The sample set contains K reward values and corresponds to each training sample in the training sample set. The ultimate goal of the algorithm is to maximise the cumulative reward.

In order to test the prediction performance of the energy consumption prediction model, the average absolute percentage error (MAPE) is used to measure the prediction accuracy. MAPE is the proportion between the error of the predicted value and the actual value, and its calculation method is shown in Eq. (17):_{i}

In order to verify the relationship between the interval m of the action space of the two recommendation algorithms and the search efficiency, the interval 0.5, 1, 2 of the action space is taken to compare the performance of the number of feedback resources of the two algorithms under different action intervals. The calculation results are shown in Table 1 and Figure 6. The data in the table represent the number of resources searched by the MRLG Rec educational resource recommendation algorithm and single educational resource recommendation algorithm in the same action space interval. During the experiment, each algorithm was independently executed 10 times, and the average value was calculated as the experimental result so as to reduce the occurrence of accidental situations. As can be seen from Table 1 and Figure 6, the interval M of action space is 0.5, 1 and 2, and the number of resources searched by the MRLG Rec educational resource recommendation algorithm is 4.85, 4.98 and 5.13. The number of resources searched by the single educational resource recommendation algorithm is 3.66, 3.75 and 3.84. In conclusion, in the same space interval, MRLG Rec search resource of the education resources recommendation algorithm is superior to the single education resources recommendation algorithm, and with the passage of action space interval, the number of search two algorithm has been increasing, widening of the gap is so MRLG Rec search advantage of the education resources recommendation algorithm is better.

Comparison of search quantity of the two recommendation algorithms when action interval M is set to different values

Recommendation algorithm | Action interval | ||
---|---|---|---|

m=0.5 | m=1 | m=1.5 | |

MRLG Rec education resource recommendation algorithm | 4.85 | 4.98 | 5.13 |

Single educational resource recommendation algorithm | 3.66 | 3.75 | 3.84 |

In order to verify the similarity accuracy index of the two algorithms for searching resources, the number of energy consumption contained in each state, _{1} and _{2}. During the experiment, each algorithm was independently executed 10 times, and the average value was calculated as the experimental result. The experimental results are shown in Table 2 and Figure 7. The data in the table represent the error between the actual value of state energy consumption and the predicted value. As can be seen from Table 2 and Figure 7, when the amount of energy consumption contained in each state

Similarity accuracy percentage of single educational resource recommendation algorithm

Recommendation algorithm | Quantity of energy consumption state | |||
---|---|---|---|---|

MRLG Rec educational resource recommendation algorithm | 81.6% | 82.2% | 82.6% | 82.9% |

Single educational resource recommendation algorithm | 86.9% | 88.3% | 88.9% | 88.9% |

This chapter shows the advantages of the educational resource recommendation system based on MRLG Rec. By establishing a calculation model and comparing the calculation results of relevant indicators, it is concluded that after combining GAN and RL modes into MRLG Rec, the final algorithm can carry out in-depth analysis on user portraits more accurately, and compared with the single educational resource recommendation system, MRLG Rec can adapt to the initial needs of user groups with large operation intensity, many times and with deep complexity, and the similarity accuracy is lower than the calculation results of a single educational recommendation system. At the same time, MRLG Rec is able to explore more teaching resources to meet the diverse needs of learning customer groups. It can be seen that the educational resource recommendation system embedded with the MRLG Rec algorithm has a more distinct advantage in the accuracy and search volume, the two most core indicators in the current teaching content recommendation field. To sum up, the following conclusions can be drawn:

MRLG Rec combined with RL and GAN can complement each other and refine the computing capability of the recommendation system. In response to the demand of personalised teaching resource recommendation, the advantages of the algorithm are highlighted. According to the action instructions of the initial state, the results of repeated reinforcement operation are recorded and screened out as feedback so as to obtain the maximum ‘reward’.

MRLG Rec recommendation system applied in education resources in the resource similarity searches and user groups in both accuracy optimum NE. Bib potential of MRLG Rec is far superior to single education resources system, which explains MRLG Rec education resources recommendation system can be competent in the situation of education mode problems and can solve the problem of cognitive confusion overload to a great extent.

Embedding the MRLG Rec algorithm into the current single educational resource recommendation system can fill the gap in database computing capability at the user level and educational resource level, and then make educational recommendation move towards a new human-computer interaction mode of personalised push.

#### Similarity accuracy percentage of single educational resource recommendation algorithm

Recommendation algorithm | Quantity of energy consumption state | |||
---|---|---|---|---|

MRLG Rec educational resource recommendation algorithm | 81.6% | 82.2% | 82.6% | 82.9% |

Single educational resource recommendation algorithm | 86.9% | 88.3% | 88.9% | 88.9% |

#### Comparison of search quantity of the two recommendation algorithms when action interval M is set to different values

Recommendation algorithm | Action interval | ||
---|---|---|---|

m=0.5 | m=1 | m=1.5 | |

MRLG Rec education resource recommendation algorithm | 4.85 | 4.98 | 5.13 |

Single educational resource recommendation algorithm | 3.66 | 3.75 | 3.84 |

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Index Evaluation Based on Logistic Distribution Fitting Transition Probability Function Children's Educational Curriculum Evaluation Management System in Mathematical Equation Model Query Translation Optimization and Mathematical Modeling for English-Chinese Cross-Language Information Retrieval The Effect of Children’s Innovative Education Courses Based on Fractional Differential Equations Fractional Differential Equations in the Standard Construction Model of the Educational Application of the Internet of Things Optimization research on prefabricated concrete frame buildings based on the dynamic equation of eccentric structure and horizontal-torsional coupling Optimization in Mathematics Modeling and Processing of New Type Silicate Glass Ceramics Green building considering image processing technology combined with CFD numerical simulation Research on identifying psychological health problems of college students by logistic regression model based on data mining Abnormal Behavior of Fractional Differential Equations in Processing Computer Big Data Mathematical Modeling Thoughts and Methods Based on Fractional Differential Equations in Teaching Research on evaluation system of cross-border E-commerce platform based on the combined model A mathematical model of PCNN for image fusion with non-sampled contourlet transform Nonlinear Differential Equations in Computer-Aided Modeling of Big Data Technology The Uniqueness of Solutions of Fractional Differential Equations in University Mathematics Teaching Based on the Principle of Compression Mapping Financial customer classification by combined model Influence of displacement ventilation on the distribution of pollutant concentrations in livestock housing Recognition of Electrical Control System of Flexible Manipulator Based on Transfer Function Estimation Method Automatic Knowledge Integration Method of English Translation Corpus Based on Kmeans Algorithm Real Estate Economic Development Based on Logarithmic Growth Function Model Design of Tennis Mobile Teaching Assistant System Based on Ordinary Differential Equations Financial Crisis Early Warning Model of Listed Companies Based on Fisher Linear Discriminant Analysis High Simulation Reconstruction of Crowd Animation Based on Optical Flow Constraint Equation Construction of Intelligent Search Engine for Big Data Multimedia Resource Subjects Based on Partial Least Squares Structural Equation 3D Animation Simulation of Computer Fractal and Fractal Technology Combined with Diamond-Square Algorithm Analysis of the Teaching Quality of Physical Education Class by Using the Method of Gradient Difference The Summation of Series Based on the Laplace Transformation Method in Mathematics Teaching Optimal Solution of the Fractional Differential Equation to Solve the Bending Performance Test of Corroded Reinforced Concrete Beams under Prestressed Fatigue Load Animation VR scene mosaic modeling based on generalized Laplacian equation Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure Optimal Model Combination of Cross-border E-commerce Platform Operation Based on Fractional Differential Equations The influence of accounting computer information processing technology on enterprise internal control under panel data simultaneous equation Research on Stability of Time-delay Force Feedback Teleoperation System Based on Scattering Matrix BIM Building HVAC Energy Saving Technology Based on Fractional Differential Equation Construction of comprehensive evaluation index system of water-saving irrigation project integrating penman Montei the quation Human Resource Management Model of Large Companies Based on Mathematical Statistics Equations Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression Analysis of technical statistical indexes of college tennis players under the win-lose regression function equation Automatic extraction and discrimination of vocal main melody based on quadratic wave equation Analysis of wireless English multimedia communication based on spatial state model equation Optimization of Linear Algebra Core Function Framework on Multicore Processors Application of hybrid kernel function in economic benefit analysis and evaluation of enterprises Research on classification of e-commerce customers based on BP neural network The Control Relationship Between the Enterprise's Electrical Equipment and Mechanical Equipment Based on Graph Theory Mathematical Modeling and Forecasting of Economic Variables Based on Linear Regression Statistics Nonlinear Differential Equations in Cross-border E-commerce Controlling Return Rate 3D Mathematical Modeling Technology in Visualized Aerobics Dance Rehearsal System Fractional Differential Equations in Electronic Information Models BIM Engineering Management Oriented to Curve Equation Model Leakage control of urban water supply network and mathematical analysis and location of leakage points based on machine learning Analysis of higher education management strategy based on entropy and dissipative structure theory Prediction of corporate financial distress based on digital signal processing and multiple regression analysis Mathematical Method to Construct the Linear Programming of Football Training Multimedia sensor image detection based on constrained underdetermined equation The Size of Children's Strollers of Different Ages Based on Ergonomic Mathematics Design Application of Numerical Computation of Partial Differential Equations in Interactive Design of Virtual Reality Media Stiffness Calculation of Gear Hydraulic System Based on the Modeling of Nonlinear Dynamics Differential Equations in the Progressive Method Knowledge Analysis of Charged Particle Motion in Uniform Electromagnetic Field Based on Maxwell Equation Relationship Between Enterprise Talent Management and Performance Based on the Structural Equation Model Method Term structure of economic management rate based on parameter analysis of estimation model of ordinary differential equation Influence analysis of piano music immersion virtual reality cooperation based on mapping equation Chinese painting and calligraphy image recognition technology based on pseudo linear directional diffusion equation Label big data compression in Internet of things based on piecewise linear regression Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation Design of language assisted learning model and online learning system under the background of artificial intelligence Study on the influence of adolescent smoking on physical training vital capacity in eastern coastal areas Application of machine learning in stock selection Comparative analysis of CR of ideological and political education in different regions based on improved fuzzy clustering Action of Aut( G ) on the set of maximal subgroups ofp -groupsResearch on loyalty prediction of e-commerce customer based on data mining Algebraic Equations in Educational Model of College Physical Education Course Education Professional English Translation Corpus Under the Binomial Theorem Coefficient Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation Semiparametric Spatial Econometric Analysis of Household Consumption Based on Ordinary Linear Regression Model Video adaptive watermark embedding and detection algorithm based on phase function equation English Learning Motivation of College Students Based on probability Distribution Scientific Model of Vocational Education Teaching Method in Differential Nonlinearity Research on mobile Awareness service and data privacy Protection based on Linear Equations computing protocol Vocal Music Teaching Model Based on Finite Element Differential Mathematical Equations Studying a matching method combining distance proximity and buffer constraints The trend and influence of media information Propagation based on nonlinear Differential equation Research on the construction of early warning model of customer churn on e-commerce platform Evaluation and prediction of regional human capital based on optimised BP neural network Study on inefficient land use determination method for cities and towns from a city examination perspective A study of local smoothness-informed convolutional neural network models for image inpainting Mathematical Calculus Modeling in Improving the Teaching Performance of Shot Put Application of Nonlinear Differential Equation in Electric Automation Control System Higher Mathematics Teaching Curriculum Model Based on Lagrangian Mathematical Model Computational Algorithm to Solve Two–Body Problem Using Power Series in Geocentric System Decisions of competing supply chain with altruistic retailer under risk aversion Optimization of Color Matching Technology in Cultural Industry by Fractional Differential Equations The Marketing of Cross-border E-commerce Enterprises in Foreign Trade Based on the Statistics of Mathematical Probability Theory Application of Linear Partial Differential Equation Theory in Guiding Football Scientific Training Nonlinear Channel Estimation for Internet of Vehicles Some Necessary Conditions for Feedback Functions of de Bruijn Sequences The Evolution Model of Regional Tourism Economic Development Difference Based on Spatial Variation Function System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model An empirical research on economic growth from industrial structure optimisation in the Three Gorges Reservoir area The Inner Relationship between Students' Psychological Factors and Physical Exercise Based on Structural Equation Model (SEM) Analysis and Research on Influencing Factors of Ideological and Political Education Teaching Effectiveness Based on Linear Equation Study of agricultural finance policy information extraction based on ELECTRA-BiLSTM-CRF Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Innovative research of vertical video creation under the background of mobile communication Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model Genetic algorithm-based congestion control optimisation for mobile data network