With the rapid development of intelligent communication technology, it has been widely used. The original learning mode of one-way acceptance of fixed teachers, fixed classes, fixed content, fixed process and fixed standard will be broken. Instead, it has transferred into a brand-new learning process where students can easily complete personalised and discovery learning in the information transmission of independent selection, reasonable acceptance, scientific processing and timely feedback with the help of computers, networks and other multimedia devices [1,2,3]. This learning mode of discovery will change the receptive learning pattern of classroom-, teacher- and textbook-centred learning.

With the gradual development of computer network technology and the successful application of artificial intelligence (AI) in the field of expert system, the development space of CAI (computer-aided instruction) is also increasing [4]. However, the adaptability of the existing CAI system is very poor that the platform cannot effectively integrate the network learning resources, and the imperfect database resources also bring in new challenges for users’ online learning. In addition, the quality and utilisation rate of online learning websites are generally low. Many websites have many problems in curriculum arrangement, functional architecture, interface design, course update frequency, reception ability and access speed, which cannot meet the actual needs of users [5,6]. The advancement of AI has made digital learning resources develop rapidly, and great changes have taken place in the types of resource carriers, transmission methods and applications, where the knowledge is represented by data, and the resources are stored, transmitted and shared with the power of the Internet [7]. At the same time, AI technology can also be effectively integrated to realise the efficient absorption and utilisation of digital resources, reflect the intellectual value of digital resources, and promote the development of digital resources system.

Learning in the era of AI is to change from traditional maintenance learning to innovative learning, from receptive learning to constructive learning. Therefore, to promote the development of foreign language education and teaching, information technology must be integrated with the English teaching, which is of great significance to the innovation of English teaching mode.

In the network-based English learning platform, learning resources are very important because they provide a rich material basis for learning activities. Generally, there are two kinds of learning resources [8, 9]: special learning resources, such as textbooks, computer operation room, etc.; assisted learning resources, such as video room, science and technology museum, etc. As shown in Figure 1, language learning resources not only take the form of words and pictures, but also include video and listening audio. In addition, it includes not only the resources obtained from the Internet and multimedia computers, but also the use of other channels, such as television, audio-visual equipment, slides, etc. Moreover, It contains resources obtained from some traditional teaching resources (such as textbooks and teaching aids), and includes equipment, applications, institutions, websites and some social and cultural organisations that can obtain the above resources.

As shown in Figure 2, language learning resources can be divided into three aspects:

Teaching materials. It refers to the object that can be directly used in learning. Further detailed explanation is consistent with the teaching plan and teaching content, carefully selected can be used in the teaching process, and can also increase all information or organisation of students’ knowledge.

Support system. It refers to all the internal or external conditions that are helpful to learning, including the support of learning motivation, learning tools, learning resources and related personnel.

Learning environment. It includes not only the place where students study, but also the atmosphere produced in the process of learning. The main feature of learning environment is the communication effect produced by the interaction of the three.

In the field of language recognition, the fusion of LSA and N-gram language models can be used to effectively extract learning resources. The core idea of this method is to adopt the strategy of linear interpolation for the probability of LSA and N-gram, where N-gram language model can predict functional words well, and LSA model can predict long-span texts well.

Latent semantic analysis (LSA) is a widely used method in the field of natural language processing [10]. It assumes that if two words appear more than once in the same document, then the two words are semantically similar. In addition, LSA constructs a matrix with a large amount of text materials, where each row of the matrix represents a word, and each column represents a document, and the element represents the number of times the word appears in the document, then singular value decomposition (SVD) is used to reduce the dimension on this matrix, in order to remove the noise and irrelevant information in the data and mine the implied semantic information. Finally, the words are expressed as vectors in K-dimensional implicit semantic space. By calculating the cosine distance of the vector, the similarity between two words is expressed, so as to extract the features of various learning resources.

The generation of a word, a sentence or an article in a natural language is regarded as a random process, in which the basic unit can be regarded as a random variable satisfying a certain probability distribution. The N-gram model takes the left and right consecutive overlapping N words as a unit. According to the context of a certain unit, the unit with the highest probability value is selected as the final result. That is to say, a word appears in the text and has a close relationship with its context. Supposing that text D(_{1}, _{2}, _{3}, ⋯ , _{i}_{n}_{1}, _{2}, _{3}, ⋯ , _{i}_{n}_{i}_{i}_{1}, _{2}, _{3}, ⋯ , _{i}_{−1}, _{i}_{+1}, ⋯ , _{n}_{i}

To solve the above problems, Markov hypothesis is usually introduced in the calculation [11, 12]. Markov hypothesis means that if the state of stochastic process at the moment _{1} is known, then the process at time _{2}(_{2} > _{1}) is only relevant to _{1}, and has no relationship with the state before _{1} moment, that is, the “no aftereffect” nature in the random process. Applying this hypothesis to natural language processing can greatly reduce the amount of calculation and the dimension of parameter space, because only a small number of vocabulary sequences need to be considered in the calculation.

The extraction of feature items by N-gram model is of great help to improve the accuracy of text classification. However, when the value of N is greater than 3, or even greater, the relationship between words can be expressed more accurately. However, to improve the accuracy, it is necessary to increase the parameter estimation, which will be more difficult than the low-order model, thus reducing the reliability of the estimated value, thereby negatively affecting the predicted performance.

In the field of language recognition, the integration of LSA and N-gram language models can be used to deal with the missing of text sentences. The core idea of this method is to adopt the strategy of linear interpolation for the probability of LSA and N-gram. N-gram language model can predict functional words well, and LSA model can predict long-span texts well, which is meaningful in realization of sentences.

For LSA, the word-document weight matrix ^{T}^{T}

That is, _{j}

To integrate LSA with N-gram language model, we adopt the strategy of linear adjustment for the probability of LSA and N-gram, as shown in Formula (3):
_{ng}

Then we use the LSA model to calculate the probability of combining the words “_{w∈V} sim(

The resource text generation algorithm based on the fusion of LSA and N-gram language model is as follows:

Input: candidate word, sentence _{1}, _{2}, ⋯ , _{k}_{c}

Calculate the sum of LSA vectors of all words in _{H}

Find vector _{H}_{m}

Use Formula (4) to calculate _{ng}_{c}

Use Formula (5) to calculate _{lsa}_{c}

The two steps above are weighted by Formula (3), and the accumulated similarity value between the correct answer and other words is recorded as Sim_{1}

Use Formula (4) to calculate _{ng}

Use Formula (5) to calculate _{lsa}

The two steps are weighted by formula (3), and the accumulated similarity value between the correct answer and other words is recorded as Sim_{2} if Sim_{1} is larger than Sim_{2}, then the candidate word is the alternative answer of the sentence; Otherwise, the candidate word is not an alternative answer.

Multi-task learning, that is, learning multiple related tasks at the same time, which makes full use of the internal relationship between tasks and can improve the learning performance of all tasks as a whole.

At present, most of the algorithms proposed for multi-task learning are based on the deformed _{1} norm, such as _{2,1} norm, and _{∞,1} norm. The _{2,1} norm is an _{2} norm operation on each row of the matrix, followed by an _{1} norm operation on the resulting result. It follows that the _{2,1} norm is really the sum of the L_2 norms of each row of the matrix. The _{∞,1} norm is the sum of the _{∞} norm of each row of the matrix. The main advantage of these two norms is that they can not only guarantee the sparsity of rows, but also achieve Group Lasso between features. Since the _{2,1} norm is convex, when the loss function is also convex, the learning problem based on the _{2,1} norm has a global optimal solution [13]. The same is true for the _{∞,1} norm.

Assuming that there are _{q}^{N}_{q}^{q}^{N}

In a typical multi-task learning model, the first ^{q}^{q}

Therefore, all the learned weight vectors form a weight matrix, which is recorded as

The goal of multi-task learning is to get the weight matrix by solving the regularization empirical risk minimization problem.

_{1,1} Norm,
_{1} norm for all the weight vectors and then sum them up to obtain the sparse solution, but it cannot find the information throughout all the tasks.

_{2,1} Norm,

In this paper, a weighted _{2,1} norm,
_{1},…, _{j}

The optimal solution algorithm is as follows:

Enter: _{0} ∈ ^{N}^{×Q}, _{0} = O_{N×Q}, _{1} = 1_{N×1}, _{1}← _{0}, _{t}

for

For each sample of _{t}_{t}_{t}_{t}

Update average gradient

Calculate the next iteration

Update weight coefficient (_{j})_{t+1} = 1 / (‖(_{jR}_{t} ‖_{2}+ ∈),

end

Output: _{T}

Based on the analysis of the application of information resource integration in language learning, multi-model integration is adopted to extract learning resources, and then the functional requirements of this system are put forward. It mainly provides a personalised learning platform for students, a personalised teaching platform for teachers, and uses the system to complete English learning tasks in the actual classroom, which realises the combination of online learning and class learning, provides teachers with a teaching mode of learning tracking analysis, and brings convenience for students to learn English anytime and anywhere.

English learning based on learning tracking analysis is mainly through checking the last learned words, checking the course summary, checking the test results, synchronise the learning space in English learning courses, vocabulary test, checking the learning plan in preparing for the exam and checking the progress in teacher management to realise the tracking and analysis of learning. For this reason, the functions of the English learning system are divided as shown in Figure 3:

Therefore, the language learning system is mainly divided into four businesses, namely, English vocabulary learning, English synchronous learning courses, test preparation learning and teacher management business, which needs to do the following in the design and implementation stage:

The system should provide practice, test and feedback for each learning content of the practice unit, and make a reasonable study plan for students;

It is necessary to provide a way to combine the learning content and training of English series;

Optimisation of learning path should be considered in the system;

Try to provide students with personalised and expandable English learning methods and provide different types of tests.

Designing the overall structure of the system is conducive to a better grasp of the system architecture. The overall structure of the online English learning system proposed in this paper is shown in Figure 4.

In the overall structure of English learning system based on learning tracking analysis, the system is mainly divided into user layer, page layer, application layer, logic layer and data layer. The user layer is divided into students and teachers. The page mainly includes English vocabulary learning page, English synchronous learning course page, exam preparation learning page and teacher management business page. The application layer mainly designs English vocabulary learning operation, English synchronous learning course operation and teacher management business operation; The search layer mainly designs English vocabulary learning and processing, English synchronous learning curriculum processing and processing; while data layer is the database of English learning system based on learning tracking analysis.

The module of English learning system designs English vocabulary learning, English synchronous learning courses, exam preparation learning and teacher management.

As shown in Figure 5, the English vocabulary learning module designs personal information management, course selection, learning words, test centre, comprehensive practice, grammar learning and leaving the classroom. Personal information management includes viewing basic information, testing records and learning records. Course selection includes viewing all courses, selected courses, newly learned words, previously known words, remaining words and last learned words; Learning words includes entering the study, handing in papers, starting the test, checking the course summary and checking the test results. The test also includes spelling practice, synchronous practice and comprehensive evaluation; Comprehensive exercises include course synchronous reading and extracurricular extended reading; Grammar also designed to check the list of grammar knowledge points, check specific knowledge points and unit quizzes.

As shown in Figure 6, the teacher management business module has designed the student roster, progress inquiry, grade checking, information sending, personal information modification and data statistics. Among them, the student roster includes modifying student information, viewing test records, refreshing courses and viewing course learning records; the progress inquiry includes selecting courses; message sending includes check my message, check the sent message.

As shown in Figure 7, the course module of English synchronous learning is designed to check the learning guide, check the directory index of knowledge map, pronunciation and vocabulary, topic and expression, reading and writing, grammar, comprehensive practice, check the result report, question and answer, vocabulary test and learning space. Among them, pronunciation and vocabulary include learning words and entering vocabulary learning; topic and expression includes playing topic and expression, viewing text, submitting topic and expression; reading and writing includes playing reading and writing, submitting reading and writing; grammar includes playing grammar and submitting grammar; comprehensive exercises include single choice, vocabulary discrimination, reading comprehension and cloze test. Learning space includes personal information, teachers’ teaching plans, class learning records and my learning records.

As shown in Figure 8, the study preparation module designs personal information, study records and study plans. Among them, personal information includes modifying personal information; learning records include learning vocabulary of the curriculum standard, learning core vocabulary, vocabulary review, English listening, cloze test, reading comprehension, English writing and comprehensive review. Learning plans include checking my learning plans.

The use of online English learning platform is helpful to reform the traditional teaching mode and provide personalised learning media for students. Based on the integration of LSA and N-gram language model, this paper puts forward an English learning resource extraction algorithm, and proposes a weighted multi-task learning model and its optimal solution for multi-task English learning, which improves the sparseness of learning variables. In addition, according to the language assisted learning model, the demand analysis, overall business and architecture of the online language learning system are designed, which mainly consists of four core modules: English vocabulary learning, English synchronous learning courses, exam preparation learning and teacher management.

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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 -groupsThe internal mechanism of corporate social responsibility fulfillment affecting debt risk in China: analysis of intermediary transmission effect based on degree of debt concentration and product market competitive advantage Study on transmission characteristics in three kinds of deformed finlines based on edge-based finite element method Asymptotic stability problem of predator–prey system with linear diffusion Research 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 sentiment analysis method based on bidirectional long short-term memory networks 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