# Optimization of English Grammar Expression Based on the Discrete Dynamic Analysis Algorithm

Authors: Yuzheng Gao; Hongli Chen
Journal: Computational Intelligence and Neuroscience
 (2022)

---

## Abstract

The dynamic changes of grammatical functions in English teaching in different language environments are different. Based on this background, this paper studies the discrete dynamic modeling technology in the big data complex system. By analyzing the current situation of the English language, this paper studies the dynamic path of the development of English functional grammar. Different from traditional modeling algorithms, dynamic modeling of complex systems can accurately process the relevant data provided by big data. This paper discusses the influence of functional grammar on English listening teaching through dynamic modeling and predictive analysis. This study reduces the error rate of the English prediction model and determines that the change of English achievement is closely related to functional grammar. The results show that the dynamic modeling of complex systems can promote the rapid development of functional grammar in English teaching and provide an effective basis for grammar research. At the same time, the dynamic prediction model based on complex system modeling can accurately predict the actual effect of English grammatical functions on improving English proficiency.


---

## Body

## 1. Introduction

Since the healthy development of teenagers has always been the focus of national attention, new technologies under the background of the big data era have been gradually applied in the education industry. With the innovation of big data technology and the rapid development of scientific progress, people gradually apply existing technologies to major industries [1]. For education, it can carry out data-driven educational decision-making, realize intelligent control of educational equipment and environment, and improve the ability of educational crisis prevention and safety management; continue to optimize teaching and learning, assist teachers to carry out accurate teaching, and assist students to achieve personalized learning; promote educational evaluation from “empiricism” to “dataism”, from “macro group evaluation” to “micro individual evaluation,” and from “single evaluation” to “comprehensive evaluation”; promote the research paradigm of social science from the sampling mode to the full-sample mode, and make social science a real empirical science. The ultimate value of education big data should be reflected in deep integration with the mainstream business of education and the continuous promotion of intelligent changes in the education system.English teaching is different from Chinese teaching. The complexity of its own grammar leads to the increase of learning difficulty. Many English words cannot be translated and used accurately and effectively [2]. In English classrooms in schools, teachers should adopt a new teaching model for English teaching [3]. Most classroom environments focus on improving the monotonous mechanical memory, and the teaching concept of the new thinking mode is also changing our learning environment [4]. Teachers have many vague introductions in English word interpretation, resulting in students’ inability to accurately understand the meaning of words, let alone use them flexibly and independently [5]. Based on the abovementioned situation, many educational researchers have proposed adding grammatical functional vocabulary to the English teaching environment. We should not only pay attention to the content of grammar, but also pay attention to the function of grammar. The function of grammatical functional vocabulary is reflected in the actual teaching links, which can improve teachers’ teaching progress and help students effectively understand and analyze the meaning of words [6, 7]. Grammatical functional vocabulary means that some verbs can directly form negative meanings behind sentences and can be extracted into the beginning of sentences to form completely different voices of two sentences [8]. For the abovementioned words, we abstract them into grammatical functional words [9]. With the excessive data flow in the big data environment, the traditional system functions can no longer meet our research needs. For the use of this vocabulary, we first use the traditional complex system to calculate its change trend [10]. Therefore, this paper proposes a complex discrete system dynamic modeling technology based on big data to study the development of grammatical functionality in English teaching and its impact [11, 12].The research structure of this paper is as follows: the first part explains the meaning of grammatical functional words in English teaching and analyzes the importance of grammar in English teaching. Then, it explores the development status of complex system dynamic modeling technology under the background of big data in various countries. The second part first shows the development trend of functional vocabulary visually, and then analyzes the development of English grammatical functions by using genetic programming dynamic modeling technology. Finally, the complex dynamic system theory is used to analyze the influencing factors of English functional vocabulary in the change of English achievement and the listening level, and its development trend and influence trend are predicted and modeled. The third part is the result analysis of the research on the dynamic modeling of the English teaching grammar function under the big data complex system, and the result analysis and prediction of the research on the impact of English grammar function on English achievement.


## 2. Related Works

In English teaching, the standardized use of grammatical content, and the changes of semantics and words are the keys to learning English well [13]. The transformation of grammar is also a great challenge to teachers’ teaching ability. For the essence of English content, semantics is a tool in people’s communication and grammar is people’s arrangement rules for the whole expression [14, 15]. With the deepening of learning English, people pay more and more attention to the functional expression of English grammar. At this time, the functional vocabulary of English grammar appears. Ordinary words help us change the order of sentences, while functional words can change the voice of the whole sentence [16]. The specific functional vocabulary includes five aspects, mainly copula and auxiliary verbs. If a word cannot form a negative meaning of a sentence or cannot be used by referring to the beginning of a sentence, the word is a nongrammatical functional word. With the help of functional grammar, teachers can reduce the interpretation time in English teaching and students can better understand the changes of sentences [17, 18]. In the context of big data, we explore the development and application of the discrete dynamic modeling technology of complex systems to English teaching grammar functional vocabulary. Dynamic modeling of complex systems was first applied in the natural field, mainly focusing on the nonlinear changes of language and the characteristics of dynamic interaction. We study the dynamic modeling of this system, which can help the rapid development of English functional vocabulary [19].The development of big data technology in the United States is relatively advanced. In complex system modeling, data-driven and automatic controls are mainly studied [20]. Due to the advantages of dynamic modeling, they mainly invest a lot in the development of the industrial field. In the process of automatic transformation of industrial gas, a gasification dynamic model is proposed by using the combination of temperature and automatic switch. It solves the problem of excessive labor costs in industrial development [21].Subway construction in Britain is an important project in urban development. Due to the large changes and wide range of urban floors, the construction environment is more complex [22]. With the uncertainty of geological factors, dynamic discrete modeling was combined in the construction process to explore and predict geological conditions, and effective results were achieved [23].Germany has also applied the complex dynamic modeling system in urban rail transit. They conducted system modeling according to the dynamic data provided by big data and obtained the real-time change factors of passenger flow [24]. The problem of high pollution and high energy consumption in urban rail transit is solved.The development of big data in China is relatively young. Since 2009, “big data” has become a popular word in the Internet information technology industry. In 2012, the Chinese government proposed the “Big Data Research and Development Plan” in the United States and approved the “12th Five-Year Plan of National Government Informatization Construction Project.” The total investment is estimated to be tens of billions, which is dedicated to five construction projects of five resource pools, including population, legal person, space, macroeconomy, and culture. The era of open, shared, and intelligent big data in China has really begun on a large scale. China has developed rapidly in the field of aerospace, and aircraft continue to expand the scope of people’s activities [25–27]. The calculation of flight trajectories is becoming more and more complex, and more data information needs to be processed. The complexity of this data will affect the normal formation of flight trajectory. Therefore, they add the trajectory data to the complex system for dynamic modeling, and finally predict the trajectory motion and calculate the flight error. Aiming at the development status of dynamic modeling of complex systems, this paper puts forward the research on functional dynamic modeling of English teaching grammar based on big data.


## 3. A Dynamic Modeling Study on the Functional Development of English Teaching Grammar and Its Impact on the English Listening Level Based on the Big Data Complex System

### 3.1. Research on Genetic Programming Dynamic Modeling of Functional Development of English Teaching Grammar Based on the Big Data Complex System

With the academic development of grammatical functional vocabulary, we study and analyze its development trend and achievements based on big data. Changing teachers’ teaching style and teaching links through the introduction of the special concept of grammatical functional vocabulary is the mainstream research direction. Adding functional vocabulary to English teaching can make students better distinguish the meaning of vocabulary, sentence structure, and voice when facing complex articles. In this paper, “English grammar” and “functional vocabulary” are used as retrieval words to search in the literature database. According to the search results, this paper analyzes the development trend and changes of grammatical functional vocabulary in recent years. Before retrieval, we also predict this development trend according to the big data complex system. A comparison of the predicted results with the actual development trend is shown in Figure1.Figure 1
Comparison between predicted results and actual development trend.As can be seen from Figure1, the academic research on functional vocabulary is still at a low ebb before 2019. It will gradually rise in 2020 and then start to grow steadily. Overall, it shows that the development of this research is in an upward period. After analyzing the development of English grammatical functionality under the complex system, we come to the conclusion that this content presents a nonlinear fluctuation trend. Complex system dynamic modeling technology is the main research technology of language discipline in the natural field. Its complexity and variability lead to its rich two-way relationship. In the study of English, the importance of grammar has changed from micro to macro. We find that the function of grammar in English teaching has a nonlinear development trend. If the research from a single discipline cannot give a full explanation, we need to build a dynamic model combined with complex systems to study and analyze this development trend.Based on the concept of the complex system in big data environment, we explore the relationship between English grammatical structure, semantic meaning, consistency characteristics, and learners’ acceptance. First, a grammar development model is established to observe the data state and variability development. In the face of these complex relations and nonlinear states, the traditional discrete dynamic model is studied according to the dynamic equations. The model structure is composed of experience and assumptions, and the parameters are obtained by numerical calculation. The abovementioned operations cannot effectively judge the development trend of grammatical functionality, and there are calculation errors. Therefore, we combine the genetic programming algorithm with dynamic modeling technology to establish the dynamic system of observation values without complete data. First, the dynamic system and related state variables are described and observed. The observation calculation formula is as follows:(1)X=x10,x20,…,xn0.The relationship of time state transformation can be expressed as the state value of the dynamic numerical group under synchronization as follows:(2)x1t+1=f1x1t,x2t,…,xnt,t.The abovementioned formula is recorded as follows:(3)FXT,t=f1XT,t,f2XT,t,…,fnXT,t.Combining the abovementioned state formulas, the transformed formula is as follows:(4)Xt+1=FXTt,t.In the discrete modeling process of the complex system, the variables ofF in the abovementioned formula are calculated to minimize the value of the following formula:(5)∑t=0m∑i=1nxit−x1∗t2.In the abovementioned formula,xit is the observed value on the time synchronization variable, and x1∗t is the final prediction result of the calculation process.In the concept of genetic programming, the population problem is generated randomly to search the desired environment, which will form the search change of the spatial range. The individual variables in the initial population are structural trees. Calculating the fitness of each individual variable is a necessary link in the principle of the evolutionary algorithm. The genetic programming algorithm contains the methods of replication, mutation, and interaction to update the population iteratively until the problem exists in a certain link, and the optimal path to solve the problem becomes the optimal solution. Solving the prediction problem is also divided into four stages. The first is to generate unique variables of related problems. Then, the fitness of independent variable individuals is calculated, and the calculation results are brought into genetic operation. Finally, the abovementioned calculation processes are overlapped repeatedly until the optimal solution of the fitness function is obtained. In the process of its resolution, we use a computer program to represent the individual variables as follows:(6)F=+,−,×,sin,cos,ln,exp,….The last ending symbol is composed of constants and variables. In the dynamic modeling system, the calculation formula of constants is as follows:(7)T=c,x1,x2,…,xn.If you want to express the final solution set, you need to form equations according to the abovementioned formula as follows:(8)x1t+1=x1t+ax1t−bx1tx2t,x2t+1=x2t+cx1tx2t−dx2t.In the abovementioned formula,x1 and x2 are dynamic system variables, and a, b, c, and d are constant quantities. When solving the population convergence rate according to genetic programming, we can use a binary tree to represent the solution of the system. In the discrete dynamic modeling system, there is no fixed requirement for the original solution set. The complete solution and hybrid method can be used to increase the calculation variables and improve the overall operation speed of the modeling system. For the binary tree calculation formula in the formula, we define it as follows:(9)xi∗t+1=fiXTt,t=fix1t,x2t,…,xnt,t.In the abovementioned formula,xi∗t+1 is the predicted value of the calculated amount in time synchronization, and the difference calculation is used to represent the original fitness value as follows:(10)fitxi=∑t=1nxi∗t−xit2.Finally, genetic operations such as cross mutations are carried out according to the calculation results. In order to prevent the iterative degradation of the number of calculations, we also need to control the number of nodes. The whole binary tree and the genetic variation process are shown in Figure2.Figure 2
The whole binary tree and the process of genetic variation.As can be seen from Figure2, each variable represents the child of a subtree, and the whole dynamic modeling system is represented by multiple variable tree groups. Crossover and mutation behavior can transform variables into new population individuals. Finally, we evaluate the dynamic model of the whole discrete modeling and evaluate the nonlinear dynamic changes of the complex system by means of error calculation. The predicted value at a certain time is defined as follows:(11)x∗t,j,0≤t≤n.The extended formula after error prediction optimization is as follows:(12)X∗t,j+1=FX∗Tt,j,t+j,X∗t,0=Xt,0≤j≤n−t.The maximum error result of defining the variablexi in synchronous prediction is as follows:(13)eip=maxxi∗t,p−xit,pxi∗t,p|0≤t,t+p≤n,0<p,p<i≤n.For the unknown state changexi, we set its error as e, then the formula can control the time synchronization within the error value as follows:(14)p=maxp|eip≤e.We set a variable to evaluate the trend error of the discrete dynamic modeling system in representing the functional development of English teaching grammar. It not only effectively reflects the evaluation system in dynamic data, but also represents the development of English functional vocabulary. According to the calculation of the abovementioned formula, the actual development of functional grammar in English teaching and the comparison results of the error rate and prediction error rate are shown in Figure3.Figure 3
Comparison of actual development error rate and prediction error rate.As can be seen from Figure3, the dynamic modeling technology under the background of big data can accurately judge the development trend of English grammatical functionality. There is little difference between the actual calculation error and the prediction error, which shows that the dynamic modeling of the complex system can solve the problems that the data affect in the calculation results in a complex environment.


### 3.2. Research on Dynamic Modeling and Prediction of the Impact of the Grammar Function on the English Listening Level Based on the Big Data Complex System

In the process of English learning, learning motivation and learning methods have a significant impact on students’ achievement. We mainly analyze the trend change of the English listening level in English teaching optimization to judge whether the overall English level of students is improved. With the use of grammatical functional vocabulary in teaching, English teaching has become more and more simple, and students’ difficulty in English learning is also decreasing. In the face of the impact and changes brought by functional vocabulary in English teaching, this paper uses the complex system big data dynamic modeling technology to explore. First, in the database-based environment, we randomly take the changes of English listening scores of students in a school as the experimental data. The English listening scores are divided into CET-4 and CET-6, and the results are compared respectively, as shown in Figure4.Figure 4
Comparison chart of grade IV and grade VI degree changes.As can be seen from Figure4, the CET-4 level of most students shows an upward trend with the increase of learning time. However, CET-6 is more difficult for students, and the growth trend is relatively slow. Facing the abovementioned survey results, teachers should change students’ learning styles and attitudes, and then help students break through the bottleneck of achievement. Based on the abovementioned situation, after adding grammatical functional vocabulary in teaching, we choose two students for experimental comparison. One learns grammatical functional vocabulary and the other does not learn grammatical functional vocabulary. The score of the student in learning functional grammar is set as A, the score of the student who does not study is set as B, and the changes of students’ scores are shown in Figure 5.Figure 5
Score change chart of learning grammatical functional vocabulary and not learning grammatical functional vocabulary.As shown in Figure5, the scores of students who join English grammar functional vocabulary learning at the CET-6 level also rise. According to the abovementioned situation, we can know that grammatical functional vocabulary in English teaching has a positive impact on the change of the English listening level, that is, it can have a relevant impact on students’ learning motivation. We choose students’ learning motivation after learning grammatical functional knowledge to predict and analyze. The time duration is three years. The motivation changes of the two students are compared, as shown in Figure 6.Figure 6
Comparison of motivation changes between two students.As can be seen from Figure6, with the growth of learning time, students who join grammatical functional vocabulary learning have positive motivation changes. Learning motivation gradually turns from the low tide to the peak. Finally, we add the influence results to the dynamic data modeling system for prediction and analysis. This paper mainly explores the influence trend of grammatical function on English achievement and the listening level. The process of the discrete dynamic system evolution model is shown in Figure 7.Figure 7
Process diagram of the evolution model of the discrete dynamic system.As can be seen from Figure7, first the system variables and calculation direction are determined, and then, the causal relationship of influencing factors is established. Finally, the dynamic equations are formed for simulation analysis. In the influence relationship, we also simulate the dynamic structure diagram of causality. The model calculation formula is as follows:(15)S=fS0+S1−S2.In the abovementioned formula,S is the scale of student participation and S0 is the number. S1 is the number of achievement growth. Finally, we introduce the linear regression model to predict the sampling results. The actual results accord with the expected values of dynamic modeling, which are able to accurately judge the impact of grammatical function on English achievement and other aspects in English teaching.


## 3.1. Research on Genetic Programming Dynamic Modeling of Functional Development of English Teaching Grammar Based on the Big Data Complex System

With the academic development of grammatical functional vocabulary, we study and analyze its development trend and achievements based on big data. Changing teachers’ teaching style and teaching links through the introduction of the special concept of grammatical functional vocabulary is the mainstream research direction. Adding functional vocabulary to English teaching can make students better distinguish the meaning of vocabulary, sentence structure, and voice when facing complex articles. In this paper, “English grammar” and “functional vocabulary” are used as retrieval words to search in the literature database. According to the search results, this paper analyzes the development trend and changes of grammatical functional vocabulary in recent years. Before retrieval, we also predict this development trend according to the big data complex system. A comparison of the predicted results with the actual development trend is shown in Figure1.Figure 1
Comparison between predicted results and actual development trend.As can be seen from Figure1, the academic research on functional vocabulary is still at a low ebb before 2019. It will gradually rise in 2020 and then start to grow steadily. Overall, it shows that the development of this research is in an upward period. After analyzing the development of English grammatical functionality under the complex system, we come to the conclusion that this content presents a nonlinear fluctuation trend. Complex system dynamic modeling technology is the main research technology of language discipline in the natural field. Its complexity and variability lead to its rich two-way relationship. In the study of English, the importance of grammar has changed from micro to macro. We find that the function of grammar in English teaching has a nonlinear development trend. If the research from a single discipline cannot give a full explanation, we need to build a dynamic model combined with complex systems to study and analyze this development trend.Based on the concept of the complex system in big data environment, we explore the relationship between English grammatical structure, semantic meaning, consistency characteristics, and learners’ acceptance. First, a grammar development model is established to observe the data state and variability development. In the face of these complex relations and nonlinear states, the traditional discrete dynamic model is studied according to the dynamic equations. The model structure is composed of experience and assumptions, and the parameters are obtained by numerical calculation. The abovementioned operations cannot effectively judge the development trend of grammatical functionality, and there are calculation errors. Therefore, we combine the genetic programming algorithm with dynamic modeling technology to establish the dynamic system of observation values without complete data. First, the dynamic system and related state variables are described and observed. The observation calculation formula is as follows:(1)X=x10,x20,…,xn0.The relationship of time state transformation can be expressed as the state value of the dynamic numerical group under synchronization as follows:(2)x1t+1=f1x1t,x2t,…,xnt,t.The abovementioned formula is recorded as follows:(3)FXT,t=f1XT,t,f2XT,t,…,fnXT,t.Combining the abovementioned state formulas, the transformed formula is as follows:(4)Xt+1=FXTt,t.In the discrete modeling process of the complex system, the variables ofF in the abovementioned formula are calculated to minimize the value of the following formula:(5)∑t=0m∑i=1nxit−x1∗t2.In the abovementioned formula,xit is the observed value on the time synchronization variable, and x1∗t is the final prediction result of the calculation process.In the concept of genetic programming, the population problem is generated randomly to search the desired environment, which will form the search change of the spatial range. The individual variables in the initial population are structural trees. Calculating the fitness of each individual variable is a necessary link in the principle of the evolutionary algorithm. The genetic programming algorithm contains the methods of replication, mutation, and interaction to update the population iteratively until the problem exists in a certain link, and the optimal path to solve the problem becomes the optimal solution. Solving the prediction problem is also divided into four stages. The first is to generate unique variables of related problems. Then, the fitness of independent variable individuals is calculated, and the calculation results are brought into genetic operation. Finally, the abovementioned calculation processes are overlapped repeatedly until the optimal solution of the fitness function is obtained. In the process of its resolution, we use a computer program to represent the individual variables as follows:(6)F=+,−,×,sin,cos,ln,exp,….The last ending symbol is composed of constants and variables. In the dynamic modeling system, the calculation formula of constants is as follows:(7)T=c,x1,x2,…,xn.If you want to express the final solution set, you need to form equations according to the abovementioned formula as follows:(8)x1t+1=x1t+ax1t−bx1tx2t,x2t+1=x2t+cx1tx2t−dx2t.In the abovementioned formula,x1 and x2 are dynamic system variables, and a, b, c, and d are constant quantities. When solving the population convergence rate according to genetic programming, we can use a binary tree to represent the solution of the system. In the discrete dynamic modeling system, there is no fixed requirement for the original solution set. The complete solution and hybrid method can be used to increase the calculation variables and improve the overall operation speed of the modeling system. For the binary tree calculation formula in the formula, we define it as follows:(9)xi∗t+1=fiXTt,t=fix1t,x2t,…,xnt,t.In the abovementioned formula,xi∗t+1 is the predicted value of the calculated amount in time synchronization, and the difference calculation is used to represent the original fitness value as follows:(10)fitxi=∑t=1nxi∗t−xit2.Finally, genetic operations such as cross mutations are carried out according to the calculation results. In order to prevent the iterative degradation of the number of calculations, we also need to control the number of nodes. The whole binary tree and the genetic variation process are shown in Figure2.Figure 2
The whole binary tree and the process of genetic variation.As can be seen from Figure2, each variable represents the child of a subtree, and the whole dynamic modeling system is represented by multiple variable tree groups. Crossover and mutation behavior can transform variables into new population individuals. Finally, we evaluate the dynamic model of the whole discrete modeling and evaluate the nonlinear dynamic changes of the complex system by means of error calculation. The predicted value at a certain time is defined as follows:(11)x∗t,j,0≤t≤n.The extended formula after error prediction optimization is as follows:(12)X∗t,j+1=FX∗Tt,j,t+j,X∗t,0=Xt,0≤j≤n−t.The maximum error result of defining the variablexi in synchronous prediction is as follows:(13)eip=maxxi∗t,p−xit,pxi∗t,p|0≤t,t+p≤n,0<p,p<i≤n.For the unknown state changexi, we set its error as e, then the formula can control the time synchronization within the error value as follows:(14)p=maxp|eip≤e.We set a variable to evaluate the trend error of the discrete dynamic modeling system in representing the functional development of English teaching grammar. It not only effectively reflects the evaluation system in dynamic data, but also represents the development of English functional vocabulary. According to the calculation of the abovementioned formula, the actual development of functional grammar in English teaching and the comparison results of the error rate and prediction error rate are shown in Figure3.Figure 3
Comparison of actual development error rate and prediction error rate.As can be seen from Figure3, the dynamic modeling technology under the background of big data can accurately judge the development trend of English grammatical functionality. There is little difference between the actual calculation error and the prediction error, which shows that the dynamic modeling of the complex system can solve the problems that the data affect in the calculation results in a complex environment.


## 3.2. Research on Dynamic Modeling and Prediction of the Impact of the Grammar Function on the English Listening Level Based on the Big Data Complex System

In the process of English learning, learning motivation and learning methods have a significant impact on students’ achievement. We mainly analyze the trend change of the English listening level in English teaching optimization to judge whether the overall English level of students is improved. With the use of grammatical functional vocabulary in teaching, English teaching has become more and more simple, and students’ difficulty in English learning is also decreasing. In the face of the impact and changes brought by functional vocabulary in English teaching, this paper uses the complex system big data dynamic modeling technology to explore. First, in the database-based environment, we randomly take the changes of English listening scores of students in a school as the experimental data. The English listening scores are divided into CET-4 and CET-6, and the results are compared respectively, as shown in Figure4.Figure 4
Comparison chart of grade IV and grade VI degree changes.As can be seen from Figure4, the CET-4 level of most students shows an upward trend with the increase of learning time. However, CET-6 is more difficult for students, and the growth trend is relatively slow. Facing the abovementioned survey results, teachers should change students’ learning styles and attitudes, and then help students break through the bottleneck of achievement. Based on the abovementioned situation, after adding grammatical functional vocabulary in teaching, we choose two students for experimental comparison. One learns grammatical functional vocabulary and the other does not learn grammatical functional vocabulary. The score of the student in learning functional grammar is set as A, the score of the student who does not study is set as B, and the changes of students’ scores are shown in Figure 5.Figure 5
Score change chart of learning grammatical functional vocabulary and not learning grammatical functional vocabulary.As shown in Figure5, the scores of students who join English grammar functional vocabulary learning at the CET-6 level also rise. According to the abovementioned situation, we can know that grammatical functional vocabulary in English teaching has a positive impact on the change of the English listening level, that is, it can have a relevant impact on students’ learning motivation. We choose students’ learning motivation after learning grammatical functional knowledge to predict and analyze. The time duration is three years. The motivation changes of the two students are compared, as shown in Figure 6.Figure 6
Comparison of motivation changes between two students.As can be seen from Figure6, with the growth of learning time, students who join grammatical functional vocabulary learning have positive motivation changes. Learning motivation gradually turns from the low tide to the peak. Finally, we add the influence results to the dynamic data modeling system for prediction and analysis. This paper mainly explores the influence trend of grammatical function on English achievement and the listening level. The process of the discrete dynamic system evolution model is shown in Figure 7.Figure 7
Process diagram of the evolution model of the discrete dynamic system.As can be seen from Figure7, first the system variables and calculation direction are determined, and then, the causal relationship of influencing factors is established. Finally, the dynamic equations are formed for simulation analysis. In the influence relationship, we also simulate the dynamic structure diagram of causality. The model calculation formula is as follows:(15)S=fS0+S1−S2.In the abovementioned formula,S is the scale of student participation and S0 is the number. S1 is the number of achievement growth. Finally, we introduce the linear regression model to predict the sampling results. The actual results accord with the expected values of dynamic modeling, which are able to accurately judge the impact of grammatical function on English achievement and other aspects in English teaching.


## 4. Dynamic Modeling of the Functional Development of English Teaching Grammar and Its Impact on the English Listening Level Based on the Big Data Complex System

### 4.1. Analysis of Research Results of Genetic Programming Dynamic Modeling Based on the Functional Development of English Teaching Grammar in the Big Data Complex System

Because the grammatical function has great advantages in explaining word meaning and sentence voice, teachers can easily summarize abstract words in teaching. In the abovementioned research process, we also find that functional grammar has developed very rapidly over the years. When applying complex system dynamic modeling to explore the development and change of grammatical functionality, this paper adopts genetic programming optimization dynamic modeling technology. Compared with traditional dynamic modeling, this technology can accurately calculate the model error. We compare the data of the two dynamic modeling techniques. In order to test the effectiveness of the algorithm in this paper, we choose the data population to rise gradually for analysis. It mainly explores the advantages and disadvantages of the two in the model error, as shown in Figure8.Figure 8
Comparison of the advantages and disadvantages of the two in the model error.It can be seen from Figure8 that although the traditional dynamic modeling technology can also process the dynamic data information, there is a large amount of error in the calculation process. Compared with the theoretical error coefficient, the dynamic modeling technology optimized by genetic programming can reduce the number of errors in the calculation process and improve the overall accuracy of the model. In exploring the development of the English teaching grammar function under the complex system of big data environment and the genetic programming dynamic data modeling, we can accurately judge the development trend of the research content.


### 4.2. Dynamic Modeling and Prediction of the Impact of Grammar Function on the English Listening Level Based on the Big Data Complex System

We randomly selected some students from the database to monitor their academic performance and learning motivation. First, we played the video of an English listening test to the learners, at the same time, and self-analyzed the difficulty and understanding of the learning content. We automatically generated the change of learning motivation after adding English grammar functional learning through the learners’ own judgment. The generated motivation intensity change waveform is shown in Figure9.Figure 9
Generated motivation intensity change waveform.As can be seen from Figure9, with the growth of learning time, the image presents a steady rise type and a fluctuation type. In understanding the learning process of steadily rising learners, we know that due to the addition of simple functional grammar, learners’ attention is also focused on the scope of the rising period. It shows that the improvement of English performance is the integration and intersection of the knowledge content, showing complex and dynamic data changes. Through communication with fluctuating learners, we know that the listening level and English level also fluctuate. Most of the factors are caused by the imperfect acceptance of grammatical functional vocabulary. For the influence trend of grammatical functionality, we establish a dynamic prediction model to explore the change of the performance prediction value from the time of learning grammatical functional vocabulary and compare the actual prediction value with the theoretical value, as shown in Figure 10.Figure 10
Comparison between the actual predicted value and the theoretical value.As can be seen from Figure10, with the increase of learning time of functional grammar, the change of English performance shows an upward trend. Compared with the actual theoretical prediction values, there is little difference, which shows that the dynamic data model of the complex system has high-precision calculation results. According to the abovementioned research results, as a theoretical calculation method, complex system dynamic modeling technology can provide data support for the functional use of grammar in English teaching. It is helpful to promote the change and development of the English language and the transformation to complexity discipline. Compared with traditional dynamic data modeling, genetic programming dynamic data modeling based on the big data complex system can ensure the accuracy of the research model.


## 4.1. Analysis of Research Results of Genetic Programming Dynamic Modeling Based on the Functional Development of English Teaching Grammar in the Big Data Complex System

Because the grammatical function has great advantages in explaining word meaning and sentence voice, teachers can easily summarize abstract words in teaching. In the abovementioned research process, we also find that functional grammar has developed very rapidly over the years. When applying complex system dynamic modeling to explore the development and change of grammatical functionality, this paper adopts genetic programming optimization dynamic modeling technology. Compared with traditional dynamic modeling, this technology can accurately calculate the model error. We compare the data of the two dynamic modeling techniques. In order to test the effectiveness of the algorithm in this paper, we choose the data population to rise gradually for analysis. It mainly explores the advantages and disadvantages of the two in the model error, as shown in Figure8.Figure 8
Comparison of the advantages and disadvantages of the two in the model error.It can be seen from Figure8 that although the traditional dynamic modeling technology can also process the dynamic data information, there is a large amount of error in the calculation process. Compared with the theoretical error coefficient, the dynamic modeling technology optimized by genetic programming can reduce the number of errors in the calculation process and improve the overall accuracy of the model. In exploring the development of the English teaching grammar function under the complex system of big data environment and the genetic programming dynamic data modeling, we can accurately judge the development trend of the research content.


## 4.2. Dynamic Modeling and Prediction of the Impact of Grammar Function on the English Listening Level Based on the Big Data Complex System

We randomly selected some students from the database to monitor their academic performance and learning motivation. First, we played the video of an English listening test to the learners, at the same time, and self-analyzed the difficulty and understanding of the learning content. We automatically generated the change of learning motivation after adding English grammar functional learning through the learners’ own judgment. The generated motivation intensity change waveform is shown in Figure9.Figure 9
Generated motivation intensity change waveform.As can be seen from Figure9, with the growth of learning time, the image presents a steady rise type and a fluctuation type. In understanding the learning process of steadily rising learners, we know that due to the addition of simple functional grammar, learners’ attention is also focused on the scope of the rising period. It shows that the improvement of English performance is the integration and intersection of the knowledge content, showing complex and dynamic data changes. Through communication with fluctuating learners, we know that the listening level and English level also fluctuate. Most of the factors are caused by the imperfect acceptance of grammatical functional vocabulary. For the influence trend of grammatical functionality, we establish a dynamic prediction model to explore the change of the performance prediction value from the time of learning grammatical functional vocabulary and compare the actual prediction value with the theoretical value, as shown in Figure 10.Figure 10
Comparison between the actual predicted value and the theoretical value.As can be seen from Figure10, with the increase of learning time of functional grammar, the change of English performance shows an upward trend. Compared with the actual theoretical prediction values, there is little difference, which shows that the dynamic data model of the complex system has high-precision calculation results. According to the abovementioned research results, as a theoretical calculation method, complex system dynamic modeling technology can provide data support for the functional use of grammar in English teaching. It is helpful to promote the change and development of the English language and the transformation to complexity discipline. Compared with traditional dynamic data modeling, genetic programming dynamic data modeling based on the big data complex system can ensure the accuracy of the research model.


## 5. Conclusion

This paper presents a dynamic study of grammar function in English teaching based on the big data complex system. This paper mainly analyzes the important role and development trend of grammatical function in English teaching, and uses genetic the programming algorithm to optimize dynamic modeling technology to explore the development of grammatical function. In complex systems, the optimization dynamic modeling technology can reduce the errors in the calculation model and has higher applicability than the traditional dynamic modeling technology. The experimental results show that in the big data environment, the complex system dynamic modeling technology can reduce the error coefficient of the calculation model and improve the accuracy of the model. Dynamic modeling technology can also accurately predict the impact of grammatical functions on English performance and English listening proficiency. Finally, according to the influence trend, it simulates the changing direction of students’ performance and provides effective technical support for English teaching. This paper solves the problem that students’ self-development has been confused in learning English. According to the student information provided by the database, this paper carries out dynamic data modeling to study the trend change and trend prediction of the abovementioned factors. However, many aspects of English teaching are still difficult to understand such as semantics, remembering words, and grammar is too complex. Therefore, further analysis and improvement are needed in future research.



---

*Source: 8742711-2022-08-12.xml*