In recent years, with the rapid development of information technology, the network has played a very important role, and even produced the situation of ‘data explosion’. A large number of complex data have different values and can be applied to the development of human science and technology. The era of big data has arrived, which provides a new way for college students’ English education and learning. It can convey learning information in a simple way. In this case, college students can more easily obtain relevant information and learn. However, contemporary college students generally lack the ability to distinguish information, which will lead them to fall in the flood of big data [1, 2]. Therefore, in the era of big data, teachers should be a qualified guide to guide college students to a bright road. However, this requires English teachers to constantly improve the teaching quality in the actual teaching process and rely on big data for teaching innovation [3, 4, 5].

In the era of big data, the method of precision teaching came into being. It relies on the technology of big data to conduct relevant teaching analysis, so as to obtain more targeted College English teaching programmes [6, 7, 8]. This innovative method has many advantages. For students, this teaching method can enable them to carry out personalised learning and give full play to their learning enthusiasm and subjective initiative. For teachers, this teaching method can help them formulate corresponding teaching plans according to the personalised characteristics of students, so as to more fully understand the current learning situation of college students in any particular course, and then improve the teaching effect [9, 10]. Therefore, precision teaching in the era of big data can promote teaching in three aspects: precision, personalisation and intelligence. It is also necessary to implement certain teaching optimisation and innovation tasks to provide scientific support for innovative education and teaching decisions.

The new era has new requirements for College English education and shoulders the mission of cultivating high-quality and international talents for national strategic development. As a compulsory course in Colleges and universities, College English teaching should keep pace with the times and use big data to innovate teaching methods, so as to cultivate more high-quality talents [11, 12].

However, compared with the traditional teaching mode, College English teaching based on big data is an era trend of combining information technology with education. In order to ensure the quality of English teaching and improve the effectiveness of English teaching, teachers need to adhere to the spirit of scientific, efficient, realistic and professional work, widely apply information technology means such as network and multimedia to English teaching, make it provide support for the development, application and maintenance of College English Teaching Courseware, further improve the quality of English teaching, and ensure that English teaching does not deviate from the development law of the times. Based on the fact that this courseware has been formulated in a such a manner as to have it retain the original advantages and characteristics of the same course in the Chinese language, we may assert that this information technology–incorporating coursework meets the new requirements for college students in the information age, especially by putting forward strategies for innovative teaching of College English by employing big data as the principal means. Precision teaching is the main innovative means. With the support of big data, innovative precision teaching aims mainly to give targeted teaching guidance according to students’ characteristics and learning behaviour, so as to improve teaching effect and promote teaching and learning [13, 14]. In the era of big data, accurate College English teaching analyses the basic information data of college students, student learning behaviour data and other data generated in the process of education and teaching, depicts student characteristics and student behaviour, and then carries out targeted teaching to improve the teaching effect. Therefore, in short, with the blessing of big data, the most important thing for innovative and accurate College English teaching is to provide targeted guidance to students.

If colleges and universities want to realise the optimal benefits of English teaching, they must start from the actual teaching situation and combine big data with English teaching. Only by recognising the advantages of College English teaching based on big data can colleges and universities give full play to its effectiveness. Generally speaking, colleges and universities rely on the network platform to innovate the teaching methods of College English courses, which can effectuate a variety of advantages.

Internet technology can bring resource sharing [15,16]. There are a lot of teaching resources in the network, which is more important for College English teaching than hardware teaching facilities such as multimedia classroom and voice laboratory. The educational resources of the network platform include various databases, teaching materials, educational information and teaching software. On the one hand, teachers can make full use of the above resources to enrich teaching content, strengthen teaching effect and share teaching results with students; on the other hand, students can flexibly select suitable teaching resources according to their own ability and learning development, and actively carry out independent learning outside the classroom. The sharing of educational resources without time and space constraints is conducive to avoiding the loss and dispersion of resources, which is of great value to college English teaching.

In classroom teaching, with the help of English original animation, film, music and other resources on the network platform, teachers can effectively build a dynamic and rich language environment, and guide students to deeply understand the knowledge points in class through immersive cultural experience and understanding. Real language situations can help students better internalise the content of English knowledge, expand the scope of their knowledge and empower them to realise an overall improvement in language ability and comprehensive quality [17]. The network platform can provide technical support for students to carry out group learning. Given a situation in which every student has the same interests and knowledge-level—and actualising such a situation in the classroom atmosphere is an effective way to give full play to the advantages of group learning—group learning can provide an enhanced learning opportunity for each student. In addition, through the network platform, students can share more knowledge and information; this platform also promotes learning and communication, and provides a convenient and fast oral and listening training platform for college students. Here, students can play English learning audio according to their own weaknesses and learning needs to realise self-learning. At the same time, students can also obtain listening, speaking and reading materials suitable for their English level from the database to meet their changing and developing learning needs. Thus, the flexible application of network platform in College English teaching provides students with rich and practical learning ways.

With the support of the network platform, some colleges and universities have enriched and innovated the English teaching mode by comprehensively considering the needs of students and the specific situation of the school. Teachers can flexibly use multimedia courseware to carry out English teaching. This mode is the most common. Teachers use electronic courseware to explain the teaching content, attract students’ attention with the help of sound and image, and enrich and vivify classroom teaching methods, the objective being to stimulate students’ subjective initiative and guide them to actively think about ways to solve problems. There is also a self-regulated learning model supported by the network platform, which is a new direction of the development of College English teaching. In addition, students can actively collect English knowledge and relevant information through network operation, and conduct independent learning and research with the help and guidance of teachers [18,19]. This model helps to improve students’ abilities in the directions of independent learning and research-oriented thinking, and cultivates in them a habit of lifelong learning.

Several innovations are needed to ensure accurate College English teaching, and the core of these efforts lies in improving the classroom teaching effect of College English, which is mainly based on students’ characteristics and behaviour [20]. The accurate description of teaching effect and students’ characteristics and behaviour is the premise of formulating accurate teaching plans, and the data and big data analysis technology generated in the process of education provide strong support for this. In general, precise College English teaching in the era of big data is not a specific teaching method. It mainly pursues a decision-making function, which can take students’ characteristics and behaviour performance as the basis for innovation, so as to formulate a set of accurate College English teaching plans. It should be precise and accurate. The College English teaching plan for each student is different. The final aim of such plans is to obtain the optimal decision function of College English teaching effect. Therefore, the formulation of accurate College English teaching plan can essentially be transformed into an optimisation problem [21].

In the optimisation process of College English teaching, the problem should be transformed into an optimal decision function

Index selection table

Teaching programme | ||

Teaching effectiveness | ||

Student characteristic performance |

For multi-stage accurate College English teaching programmes, the characteristics of students will be different in different stages, and thus the application programmes formulated in the process of College English teaching will also become different [11]. The College English teaching process of the study consists of _{k}_{k}_{k}_{k}_{k}_{k}_{k}_{k}

It can be seen that the teaching scheme _{K}_{K}

_{K}

Here, _{K}_{1}, …,_{K}_{k}_{1}, …, _{k}_{k−1} = (_{1}, …, _{k}_{−1}) indicates all the teaching programmes adopted before stage _{k}_{k}_{k−1}) represents the history up to stage

If the decision functions at different stages are represented by a vector _{1},_{2}, …,_{K}_{K}_{1},_{2},…,_{k}

However, the solution to this optimisation problem of College English teaching is based on the accurate description of the effect of College English teaching and students’ characteristics pertaining to their performance [24]. However, the accurate description of College English teaching effect and students’ characteristics needs a lot of data as support. In addition, the description and selection methods of the model in this process also need a lot of data to run. In other words, all kinds of optimisation problems need data to process and solve. In general, the approach used is to systematically analyse the data generated in the process of College English teaching by using relevant statistical methods and big data analysis technology in combination with the basic requirements involved in the process, extract the characteristics of students and then explore the internal development occurring in the process, so as to formulate accurate College English teaching plans and improve the quality of College English teaching, and simultaneously ensure that this teaching method based on big data also proves useful towards application in the teaching of other subjects.

In order to analyse the optimal selection and fusion clustering of College English teaching mode, combined with the statistical big data analysis method of College English teaching mode, the analysis model of the optimal fusion characteristic parameters of College English teaching mode is constructed by using big data mining technology, and the data mining model of College English teaching mode is constructed by using adaptive association rule mining method, The phase space distribution _{q}_{i}_{k}_{k j}_{i}

Here _{i},v_{j}_{i}_{j}

Here _{i}_{i}

Here
_{m}_{μ}_{m}_{μ}

The nonlinear time series analysis method is used to analyse and predict the characteristics of College English teaching model statistical big data. Through the preliminary statistical value of College English teaching mode statistical big data, the constraint index parameter set of teaching mode optimisation is analysed in embedded system teaching, and the correlation detection method is used for big data fusion processing of College English teaching optimal mode. The output rules of College English teaching mode statistical big data are obtained by using multiple regression analysis method:

Here _{j}_{n}_{j}_{j}_{1} and _{2}. In the _{g}_{j}_{1} and _{2} of the statistical big data of College English teaching model are determined by Eq. (11):

Here _{1}, _{2} is _{1} is the association rule vector set of statistical big data of College English teaching model and _{2} is the characteristic quantity of diameter distribution. According to the above analysis, the feature extraction and fuzzy scheduling of College English teaching mode statistical big data are realised, and the ability of feature extraction and quantitative regression analysis of College English teaching mode statistical big data is improved by combining the optimal mode selection method. The next step is to carry out the statistical data fusion processing of the optimal model of College English teaching, for which we construct the English teaching model statistical big data time series {_{0} +

Among,
_{μ}_{w}_{μ}

Here _{n}

The matching detection of cantaloupe is carried out in the decentralised subspace to improve the ability of optimal pattern selection. Finally, the cluster selection of the optimal mode of College English teaching should be carried out. The fuzzy convergence control function of statistical big data of College English teaching mode is given as follows:

In Eq. (17), the load predicted by big data of College English teaching model statistics is _{h}

According to the above discriminant conditions, a support vector machine model is constructed, and _{u,v} is used to represent the fuzzy set quality of College English teaching model statistical big data attribute set. For College English teaching mode statistics, the association rule attribute of big data scheduling is _{0}_{u,v}_{1},_{2}). Based on the clustering results, the quantitative distribution set of the optimal model of College English teaching is constructed [28]. Combined with the big data fusion method, the optimal model of College English teaching is constructed, and the limited data set of statistical big data sampling of College English teaching model is obtained:

There are _{i},i

The modified support vector machine learning model is used to design the classifier of College English teaching mode, and the characteristic distribution matrix of the classification of College English teaching mode statistical big data is obtained, which meets the following requirements:

Then, we construct the number chain of statistical big data distribution of College English teaching model in continuous finite state space _{ij}_{N*N}. Combined with the fusion method of big data, the construction of the optimal model of College English teaching is realised, and the statistical characteristic quantity is as follows:

If Δ > 0 and _{ij}

Combined with the optimal mode selection method, this paper realises the empirical test and analysis of the optimal mode of College English teaching.

College English teachers should recognise the importance of network informatisation, make good use of the network platform in the era of big data and information, and promote the innovative development of English teaching. Educators need to give full play to their subjective initiative and complete various teaching tasks on the premise of improving teaching quality. Group teaching is based on the principle of flexible setting of students’ teaching situation, and group teaching must follow the basic direction of students’ initiative. Therefore, it is necessary for College English teachers to design teaching methods that can arouse students’ interest and improve students’ creativity from the perspective of students’ development and combined with students’ actual needs [29,30]. It should be noted that teachers must maintain an objective understanding of network information-based teaching, establish systematic and scientific curriculum standards, and implement teaching tasks according to the established objectives and plans, so as to avoid a situation wherein English teaching that relies on the network platform cannot give full play to its advantages due to the deviation of understanding. In addition, College English teachers should combine the advantages of network information teaching with the objectives of English teaching courses, build an efficient teaching model on this basis and introduce this new teaching model into classroom teaching practice.

The rapid global development of social science and technology, as well as the fact that students need to have an ability to grasp—especially scientific and technological—research findings generated around the world to keep their knowledge abreast of the developments in various fields, indicates that the ability to understand and express oneself in English is a major concern, and therefore, College English occupies a very important position. The era of big data has thus imposed higher requirements for the teaching of English. In order to improve the teaching quality of College English, innovative strategies are needed. This paper improves the mathematical model of accurate teaching strategy and establishes the empirical model of the optimal model of College English teaching based on big data. It also puts forward relevant innovative teaching plans, improves the content of College English classroom teaching knowledge through the rational use of big data technology and updates the form of College English classroom teaching, so as to achieve improvements in the efficiency of English classroom learning, the comprehensive learning effect of college students’ English and the learning quality with which college students imbibe a knowledge of English that would help them in enhancing their professional skills and careers in the future.

#### Index selection table

Teaching programme | ||

Teaching effectiveness | ||

Student characteristic performance |

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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