# Big Data Analysis and Modeling of Higher Education Reform Based on Cloud Computing Technology

Authors: Ziye Tang
Journal: Security and Communication Networks
 (2022)

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## Abstract

In view of the current educational environment, the development of higher education cannot be separated from the support of information technology. The era of big data has had various impacts on higher education and accelerated the development of higher education. Cloud computing technology has good application prospects for the data analysis and mathematical modeling content involved in the reform of higher education. This study analyzes and calculates the concerns of the four main contents involved in the educational reform of a higher education institution as the research object. In order to quantify the application effects of different cloud computing technologies, the paper introduces three typical cloud computing technologies to analyze the research data and establish related mathematical models. The results show that the prediction accuracy based on the immune cloning algorithm is the best. Based on the calculation results, it can be found that the corresponding data of the two main contents of teaching scene setting and teaching quality detection show an obvious polynomial function relationship.


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## Body

## 1. Introduction

The traditional teaching model [1, 2] is a one-to-many classroom teaching pattern. That is, the instructor instills knowledge unilaterally to the recipient face-to-face in a fixed place and time. This model of teaching is limited by time and space and has been used to this day. At present, much of our university education still follows the traditional teaching model, i.e., uniform classes and uniform examinations. It is difficult to explore students’ differences and interests in this “one size fits all” model, resulting in a lack of innovation in the final talent produced [3]. China has always advocated the concept of education according to the needs of the individual, but it is difficult to achieve this due to the limitations of educational resources and traditional teaching models.After years of development, many universities have formed a unique way of development. However, they are still accustomed to investing only in the construction of hardware and improvement of teaching quality, ignoring the importance of modern education management [4]. This leads to an overall low level of educational management that does not meet the basic development needs of the new-age students. For example, the awareness of information technology education management is weak, and the scope and strength of information technology construction are limited [5, 6]. The development of an education management information systems is not in place. When a bottleneck is encountered at work, development work is put on hold. The education management system is not sound, and the education management work lacks norms and standards. The construction of education management team is insufficient, and the information operation level of managers is low, so they cannot carry out management work effectively. This shows that the status quo of higher education management informatization cannot meet the needs of higher education reform. Therefore, the construction of higher education management information technology is urgent.With the rapid development of technology and great progress in the twenty-first century, mankind has entered a new era of intelligence. Artificial intelligence has entered all corners of human social life, and the development of artificial intelligence has become a major strategic opportunity for various countries at present. How to occupy the technological frontier of the development of artificial intelligence and turn it into an important breakthrough for China to lead the world in the future has become a priority task at present. The era of artificial intelligence requires not only capturing breakthroughs in technological advances but also cultivating specialized talent. Therefore, in this context, it is also of utmost importance to plan and develop the artificial intelligence education system, further promote the change in higher education, and jointly speed up the development of artificial intelligence, high-speed Internet and education big data, and other related disciplines [7, 8].Currently, with the rapid development of information technology such as the Internet, cloud computing, and cloud storage, the speed of data update and dissemination has increased exponentially. The challenge of how to store, process, and share this data is enormous. It is in this context that big data emerged. Big data [9, 10] is not limited to digital but includes video, text, images, and audio in many forms. Big data is widely used in higher education reform, and the application of big data technology has also accelerated the process of education reform.With the further development and advancement of technology, innovative technologies [11, 12] such as big data, cloud computing, and artificial intelligence are widely used in different fields such as science and technology. For example, traditional smart technologies [13, 14] are used in the prediction and research process in various fields. By simulating the replication process of human chromosomes, the genetic algorithm forms a new nonlinear optimization algorithm, which optimizes the fixed weights and thresholds in the traditional neural network. In this way, the calculation purpose of finding the optimal solution is achieved. The particle swarm optimization algorithm completes the nonlinear calculation process by simulating the social behavior of different quasi-groups. By setting a certain optimal value threshold, the algorithm finds the individual optimal solution and the global optimal solution through the movement of particles. At the same time, compared with genetic algorithms, particle swarm optimization has stronger adaptability.Similarly, big data and cloud computing technologies can be applied to analysis and modeling efforts for higher education reform. In this study, from the perspective of big data technology, by introducing several conventional artificial intelligence techniques, we will make a nonlinear mathematical model prediction from several aspects of the curriculum, teaching content, teaching scenario setting, and teaching quality detection involved in higher education. And by comparing the predictive performance of several intelligent algorithms, we can provide suggestions on the process of educational change in Chinese higher education.


## 2. Exploration of Higher Education under Cloud Computing and Big Data Analysis

Higher education in the era of big data and artificial intelligence should use the analysis function of big data and pay more attention to teaching according to students’ differences for one-to-one independent training [15, 16]. At the same time, we also need to provide a precise training platform for the development of students’ innovation and cooperation.At present, much of our university education still follows the traditional teaching model, i.e., uniform classes and uniform examinations. It is difficult to explore students’ differences and interests in this “one size fits all” model. Big data provides a more comprehensive picture of each student by collecting, analyzing, and providing feedback on a variety of campus educational data. However, the educated are living individuals whose interests may change over time. Therefore, we need to continuously change and adjust our teaching plan according to the actual situation after receiving the data.However, if we want to successfully implement the above reform of the new teaching model of higher education based on big data technology, it is inseparable from the supporting role of data informatization [17, 18]. With the continuous promotion of information technology construction, related work has penetrated all aspects of teaching, research, service, management, culture, and life. There is a large gap between the rough information technology operation and maintenance services, passive network security supervision, and the demands of teachers and students. Similarly, big data in higher education is also built based on information technology. Figure 1 shows the framework of campus big data governance support platform construction.Figure 1
Construction framework of the campus big data governance support platform.As shown in Figure1, the smart campus integrates teaching, research, and management based on the digital campus. Based on mobile Internet and intelligent sensing network and supported by big data and cloud computing [19, 20], the gap between the virtual campus and the physical campus is narrowing, and the activities of school teachers and students have covered the old physical space and the emerging digital space. Campus security, on-campus management, teacher and student classrooms, learning modes, teaching methods, and office processes will all be gradually made smart.In the context of a smart campus, the information technology within the university is becoming more and more mature, which brings a lot of convenience for various information processing. The management of information no longer takes a lot of time to carry out the process of issuing many documents. Compared with traditional teaching, the reform of university education based on cloud computing can be taught by a coordinated and unified approach of online courses and practical courses. Web courses can use a variety of technologies to present and process the teaching content, including language, text, pictures, and other delivery methods. The special course content can be carried out in the form of online teaching lectures, which not only can eliminate many objective conditions, but also can be independent of time and geographical influence. This can fully reflect the distribution characteristics of various types of information for students.In the era of big data, teachers are the initiators and organizers of teaching, and they play a key role in the middle of whether online teaching can be realized and how effective it can be. In the information age, students have access to a wide variety of online information, which includes information that is both beneficial and unhelpful to them. If students are not properly guided in this process, they are likely to spend a lot of time-consuming poor quality information and thus ignore useful information. Therefore, in the face of a large amount of online information, the teacher’s job is no longer just to impart knowledge, but to guide students on how to search and identify useful information, so that they can feel the charm of independent learning, which undoubtedly puts higher demands on the teacher’s ability. Therefore, teachers are required to familiarize themselves with online knowledge learning one step ahead of time, and then guide students in independent learning. The teacher should help students when they encounter problems so that they can eventually develop the ability to learn independently.In the era of big data, higher education management based on information tools is the focus of educational reform efforts. Facing the shortcomings of current higher education management, colleges and universities should base on the actual development needs of students, strengthen the information construction of higher education management through various means, and give full play to the advantages of big data technology and information technology. This requires schools to provide more comprehensive education management services for students by starting from various aspects such as education management system, education management reform, faculty construction, and financial investment.The original goal of cloud computing is the management of resources. However, the management is mainly of computing, storage, and network resources. Big data technology, or artificial intelligence algorithms, is an important part of the cloud computing platform, and is mainly responsible for the scheduling work in cloud computing. Limited by the length of the article, this article is based on the application of artificial intelligence algorithms in cloud computing. However, it should be pointed out that the implementation of cloud computing is the result of the interaction of various computing modules. This approach is also applicable to reform applications in higher education. In order to specifically illustrate the application effect of big data technology in higher education reform, taking the education reform of a higher education institution as an example [21, 22], we compare the attention of the main content of curriculum setting, teaching content, teaching scene setting, and teaching quality inspection involved in this reform. In order to complete the above-mentioned prediction tasks, the following study systematically expounds on the application of big data technology in this education reform by introducing three cloud computing methods: immune cloning algorithm, fuzzy neural inference system, and Elman network. In addition, we can also obtain the model equation between any two or more main contents by means of mathematical fitting.It should be noted that cloud computing is carried out on the basis of big data. That is to say, the big data-driven environment is the data matrix of cloud computing. Without the help of big data, it is impossible to build a cloud computing platform smoothly.In order to clearly express the application effect of cloud computing technology in education reform, we have elaborated the following ideas. Section3 mainly introduces several computing methods of cloud computing. The article compares the application of three cloud computing methods through Section 4, and obtains the model equation between teaching scene setting and teaching quality detection through the mathematical fitting.


## 3. An Introduction to Cloud Computing Methods

### 3.1. Immune Cloning Algorithm [23, 24]

The biological immune system is a complex adaptive system. The human immune system can recognize pathogens and respond to them, so it has certain abilities of learning, memory, and pattern recognition. This way is similar to an external stimulating antigen to stimulate the human immune system to produce antibodies adapted to it. That is to say, an input variable corresponds to a unique output function. In this way, the principle and mechanism of its information processing can be described by computer algorithms to solve scientific and engineering problems. Algorithmic immunity retains some characteristics of the biological immune system and introduces them to solve optimization problems.A population suppression process is added to the immune algorithm to control the average concentration of the population and avoid premature convergence of the algorithm to a locally optimal solution. This increases the global optimization capability.A typical multi-peak function is used to enhance the application of the immune algorithm. The multi-peak function can be expressed as follows.(1)gy=∑i=1n−1100yi+1−yi22+1−yi2.In the formula,g(y) represents the fitness function involved in the immune algorithm.The global minimum point of the multimodal function is obtained when all the independent variables are 1, and the minimum value of the function is 0. The search interval for the independent variable is−10,10. The optimization results of five times of immune algorithm trial calculation are shown in Figure 2. It can be seen from Figure 2 that the immune algorithm has a good ability to search for multi-dimensional and multi-peak functions.Figure 2
Function optimization results.In the process of building a multi-dimensional support vector machine prediction model, the values of the control parameters need to be specified artificially to control the parameter values to achieve the minimum sample training error and the best multi-dimensional support vector machine model generalization accuracy.In the model training phase, the overall error function of the set of training samples is defined as the optimization objective, and the same insensitive loss function is used for the errors of individual samples. The training samples are divided into learning samples and test samples, and the expression of the normalized objective function is:(2)C∗,ε∗,σ∗=argminC,ε,σLallC,ε,σ,LallC,ε,σ=∑m=1k∑i=1kmLui,where LallC,ε,σ denotes the overall training loss function, k denotes the number of aliquots of the sample, km denotes the number of each copy after k aliquots of training samples, and the superscript asterisks indicate the optimal parameters obtained.After the training of the prediction model based on the immune algorithm is completed, that is, after the optimal engineering parameters are obtained by the immune algorithm, the calculation process of the entire artificial intelligence algorithm can be considered to be completed. Figure3 shows the operation process of the immune algorithm cloning algorithm.Figure 3
Flowchart of immune cloning algorithm.The range of the error value can be judged by the inversion theory of the immune algorithm. The specific error function can be expressed as follows.(3)x∗=argminxaffx,affx=fux−u2+fvx−v2.In the formula,x is the parameter vector to be inverted, fux is the predicted convergence value of the immune cloning algorithm model, fux is the predicted value of the immune algorithm model, u is the measured value of the No. 1 site, and v is the measured value of No. 2 site. aff is the affinity function (minimization problem).In the optimization process of the immune cloning algorithm, in order to expand the search range and efficiency of the parameters, the parameters to be optimized are mapped exponentially, that is, the value range of the parameters in the population is the natural logarithm of the actual value range. The concrete realization model equation of this calculation process is as follows.(4)Q′=expQ.In the formula:Q is the value of each individual. The affinity calculation adopts the individual value after index mapping. expQ represents the form of an exponential function of a variable.The specific operation steps of the immune cloning algorithm are shown in Figure3. As shown in Figure 3, the immune algorithm achieves certain computational goals by setting certain preconditions. Algorithms can then obtain their own unique principle calculation equations through built-in nonlinear calculation rules. Finally, several parameters to be determined in the immune algorithm can be determined through a certain optimal condition setting method.


### 3.2. Prediction Based on Fuzzy Neural Inference System

The neural reasoning system [25] is a system composed of three components, which mainly include (1) rule base (2) database (3) reasoning system. In the fuzzy neural inference system, the input parameters consider different fuzzification and defuzzification methods and strategies and have various rules. This intelligent algorithm can choose from many sets of member functions to ensure the effect of fuzzy logic on the input data. The fuzzy inference system can be divided into three inference modes according to the “if-then rule” inference operation. These inference modes are Mamdani system, Sugeno system, and Tsukamoto system, respectively. Sugeno system is considered to be the most popular candidate for sample-based fuzzy modeling and facilitates the use of adaptive techniques. In a one-dimensional Sugeno system, a typical rule set with two computational rules for fuzzy inference can be expressed as follows.Whenx = A1, y = B1, then the fuzzy neural inference system can get:(5)f1=α1x+qβ1y+η1.In the formula,f1 represents the function value obtained by the fuzzy neural inference system, α1, β1, and η1, respectively, represent the undetermined coefficients related to the function output.Similarly, whenx = A2, y = B2, then the fuzzy neural inference system can get:(6)f2=p2x+q2y+r2.In the formula,f2 represents the function value obtained by the fuzzy neural inference system, α2, β2, and η2, respectively, represent the undetermined coefficients related to the function output.Figure4 is a schematic diagram of the workflow of the fuzzy neural network prediction system.Figure 4
Elman network structure diagram.


### 3.3. Prediction Based on Elman Network

Neural networks are widely used for their large-scale parallel distributed structure, learning ability, and generalization ability. The main advantages are nonlinear analysis capability, convenient input/output mapping, adaptive capability, evidence response, background information, strong fault tolerance, VLSI (Very Large Scale Integrated) implementation, analysis and design consistency, and neural biological analogy. This study takes Elman neural network as an example to describe the implementation process of traditional neural network prediction in detail.The calculation process of the Elman network can be expressed as follows.For the input layer, the Elman network can be represented as follows.(7)xi0=xik.For the hidden layer, the Elman network can be expressed as follows.(8)si1=∑j=1n0wij0xj0k+∑j=1n1wij2cj0k,xi1=f1si1k.For the association layer, the Elman network can be expressed as follows.(9)si2k=xi1k−1,cik=si2k.For the output layer, the Elman network can be represented as follows.(10)si3k=∑j=1n1wij1xj1k,yik=f2si3k.The key to the nonlinear ability and learning ability of the network lies in the continuous correction of the weights. There are two methods for recurrent network training, one is batch mode, and the other is the online mode, Elman network adopts the latter.


## 3.1. Immune Cloning Algorithm [23, 24]

The biological immune system is a complex adaptive system. The human immune system can recognize pathogens and respond to them, so it has certain abilities of learning, memory, and pattern recognition. This way is similar to an external stimulating antigen to stimulate the human immune system to produce antibodies adapted to it. That is to say, an input variable corresponds to a unique output function. In this way, the principle and mechanism of its information processing can be described by computer algorithms to solve scientific and engineering problems. Algorithmic immunity retains some characteristics of the biological immune system and introduces them to solve optimization problems.A population suppression process is added to the immune algorithm to control the average concentration of the population and avoid premature convergence of the algorithm to a locally optimal solution. This increases the global optimization capability.A typical multi-peak function is used to enhance the application of the immune algorithm. The multi-peak function can be expressed as follows.(1)gy=∑i=1n−1100yi+1−yi22+1−yi2.In the formula,g(y) represents the fitness function involved in the immune algorithm.The global minimum point of the multimodal function is obtained when all the independent variables are 1, and the minimum value of the function is 0. The search interval for the independent variable is−10,10. The optimization results of five times of immune algorithm trial calculation are shown in Figure 2. It can be seen from Figure 2 that the immune algorithm has a good ability to search for multi-dimensional and multi-peak functions.Figure 2
Function optimization results.In the process of building a multi-dimensional support vector machine prediction model, the values of the control parameters need to be specified artificially to control the parameter values to achieve the minimum sample training error and the best multi-dimensional support vector machine model generalization accuracy.In the model training phase, the overall error function of the set of training samples is defined as the optimization objective, and the same insensitive loss function is used for the errors of individual samples. The training samples are divided into learning samples and test samples, and the expression of the normalized objective function is:(2)C∗,ε∗,σ∗=argminC,ε,σLallC,ε,σ,LallC,ε,σ=∑m=1k∑i=1kmLui,where LallC,ε,σ denotes the overall training loss function, k denotes the number of aliquots of the sample, km denotes the number of each copy after k aliquots of training samples, and the superscript asterisks indicate the optimal parameters obtained.After the training of the prediction model based on the immune algorithm is completed, that is, after the optimal engineering parameters are obtained by the immune algorithm, the calculation process of the entire artificial intelligence algorithm can be considered to be completed. Figure3 shows the operation process of the immune algorithm cloning algorithm.Figure 3
Flowchart of immune cloning algorithm.The range of the error value can be judged by the inversion theory of the immune algorithm. The specific error function can be expressed as follows.(3)x∗=argminxaffx,affx=fux−u2+fvx−v2.In the formula,x is the parameter vector to be inverted, fux is the predicted convergence value of the immune cloning algorithm model, fux is the predicted value of the immune algorithm model, u is the measured value of the No. 1 site, and v is the measured value of No. 2 site. aff is the affinity function (minimization problem).In the optimization process of the immune cloning algorithm, in order to expand the search range and efficiency of the parameters, the parameters to be optimized are mapped exponentially, that is, the value range of the parameters in the population is the natural logarithm of the actual value range. The concrete realization model equation of this calculation process is as follows.(4)Q′=expQ.In the formula:Q is the value of each individual. The affinity calculation adopts the individual value after index mapping. expQ represents the form of an exponential function of a variable.The specific operation steps of the immune cloning algorithm are shown in Figure3. As shown in Figure 3, the immune algorithm achieves certain computational goals by setting certain preconditions. Algorithms can then obtain their own unique principle calculation equations through built-in nonlinear calculation rules. Finally, several parameters to be determined in the immune algorithm can be determined through a certain optimal condition setting method.


## 3.2. Prediction Based on Fuzzy Neural Inference System

The neural reasoning system [25] is a system composed of three components, which mainly include (1) rule base (2) database (3) reasoning system. In the fuzzy neural inference system, the input parameters consider different fuzzification and defuzzification methods and strategies and have various rules. This intelligent algorithm can choose from many sets of member functions to ensure the effect of fuzzy logic on the input data. The fuzzy inference system can be divided into three inference modes according to the “if-then rule” inference operation. These inference modes are Mamdani system, Sugeno system, and Tsukamoto system, respectively. Sugeno system is considered to be the most popular candidate for sample-based fuzzy modeling and facilitates the use of adaptive techniques. In a one-dimensional Sugeno system, a typical rule set with two computational rules for fuzzy inference can be expressed as follows.Whenx = A1, y = B1, then the fuzzy neural inference system can get:(5)f1=α1x+qβ1y+η1.In the formula,f1 represents the function value obtained by the fuzzy neural inference system, α1, β1, and η1, respectively, represent the undetermined coefficients related to the function output.Similarly, whenx = A2, y = B2, then the fuzzy neural inference system can get:(6)f2=p2x+q2y+r2.In the formula,f2 represents the function value obtained by the fuzzy neural inference system, α2, β2, and η2, respectively, represent the undetermined coefficients related to the function output.Figure4 is a schematic diagram of the workflow of the fuzzy neural network prediction system.Figure 4
Elman network structure diagram.


## 3.3. Prediction Based on Elman Network

Neural networks are widely used for their large-scale parallel distributed structure, learning ability, and generalization ability. The main advantages are nonlinear analysis capability, convenient input/output mapping, adaptive capability, evidence response, background information, strong fault tolerance, VLSI (Very Large Scale Integrated) implementation, analysis and design consistency, and neural biological analogy. This study takes Elman neural network as an example to describe the implementation process of traditional neural network prediction in detail.The calculation process of the Elman network can be expressed as follows.For the input layer, the Elman network can be represented as follows.(7)xi0=xik.For the hidden layer, the Elman network can be expressed as follows.(8)si1=∑j=1n0wij0xj0k+∑j=1n1wij2cj0k,xi1=f1si1k.For the association layer, the Elman network can be expressed as follows.(9)si2k=xi1k−1,cik=si2k.For the output layer, the Elman network can be represented as follows.(10)si3k=∑j=1n1wij1xj1k,yik=f2si3k.The key to the nonlinear ability and learning ability of the network lies in the continuous correction of the weights. There are two methods for recurrent network training, one is batch mode, and the other is the online mode, Elman network adopts the latter.


## 4. Example Verification and Analysis

As mentioned in the second section of the article, this study uses the corresponding concerns of the four main aspects involved in the teaching reform of a certain institution of higher learning as the research data for research. The prediction effect of the three cloud computing methods mentioned above is mainly analyzed. In addition, by comparing the relevant data corresponding to the four main aspects, we can also obtain the model equations of any two main aspects.These data mainly include the attention corresponding to four main aspects. For the data collection of the educational reform of a certain institution of higher learning, the number of data for each item is 5800 groups. The number of data in four data groups is as high as 23200 groups.For the content of the curriculum, big data technology can use information collection and big data analysis to group the students enrolled in the major categories according to their learning willingness and career planning. In addition, it can also plan a professional orientation for each student through questionnaires and analysis of students’ class performance. In addition, this model can set up an optimized curriculum system that is conducive to their academic development according to different professional orientations.For teaching content, big data technology relies on Internet big data, continuously integrates the content of other related disciplines, and continuously integrates foreign advanced knowledge content. In addition, on the basis of existing educational resources, this technology can expand the proportion of course cases that solve international-related problems, and truly improve students’ professional quality and technical level in dealing with international problems.For the setting of teaching scenarios, various new methods and technologies in the era of artificial intelligence are the entry point to change the barriers of the old education system. Through the combination and application of various teaching methods, higher education courses can go out of traditional classrooms. This approach allows students to make the most of every minute around them. In addition, it has established comprehensively diversified and multi-level online teaching resources through introduction or creation.For teaching quality detection, big data technology can use the Internet and big data to closely combine education evaluation with artificial intelligence by tracking and monitoring the whole process of teaching and learning. At the same time, it can analyze various student learning behavior data obtained from the network and the terminal, and realize comprehensive evaluation based on the data.The three artificial intelligence techniques discussed above are mostly used in this section to conduct research on higher education reform modeling, which may serve as a theoretical foundation for the analysis and modeling of future higher education models. The suggested model is capable of predicting, analyzing, and computing attention data in curriculum development, teaching content, teaching scenario development, and teaching quality detection.The comprehensive impact matrix among the four main contents involved in higher education reform can be drawn in Figure5. It can be found from Figure 5 that the influence degree of each main content is different, and the difference is large. This illustrates the need to add artificial intelligence-like techniques to the prediction process for such problems. As shown in Figure 5, we can also find that the predicted values of the four main impact indicators have good continuity and derivation. This also shows from the side that the prediction effect based on the cloud computing method is relatively complete, and can approximately reflect the distribution law of the original data.Figure 5
Influence index value of four main contents.The elements of each row in the comprehensive influence matrix are added to obtain the corresponding influence degrees of curriculum setting, teaching content, teaching scene setting, and teaching quality detection. The sum of the degree of influence and the degree of being influenced is the degree of centrality. The greater the degree of centrality, the stronger the effect of this research project on the research target. The difference between the degree of influence and the degree of being influenced is the degree of cause. The smaller the causal degree, the more likely the influencing factor is to be influenced by other influencing factors, which is called the result factor. Figure6 is a graph showing the variation curve of the degree of cause corresponding to the detection of teaching quality with the calculation time step.Figure 6
Influencing factors of four feature columns.The degree of cause can reflect the categories of influencing factors. The larger the causality degree of the influencing factor, the more the factor is the causal index in the influencing factor system. The smaller the causal degree of the influencing factor, the more the factor is the result index in the influencing factor system. As shown in Figure6, as the calculation time step increases, the cause degree corresponding to the teaching quality detection increases first and then decreases, and when the time step is approximately equal to 195, the value of the cause degree reaches the maximum value. Its maximum value is about 4.5.When the number of big data computing steps is 4000, 8000, 12000, 16000, and 20000, respectively, we compare the calculation completion time of the three algorithms: immune cloning algorithm, fuzzy neural inference system, and traditional Elman network. The specific test results are shown in Figure7.Figure 7
Comparison of task completion time.It can be seen from Figure7 that compared with the three methods of immune cloning algorithm, fuzzy neural reasoning and traditional Elman network, the corresponding computing time of immune cloning algorithm is shorter, and the prediction is better. As the time required to complete the task increases, the difference in the completion time corresponding to the three algorithms becomes more and more obvious. Among them, the advantages shown by the immune algorithm are more obvious.It is well known that among the evaluation indicators of various prediction algorithms, the coefficient of determination (r2) and the root mean square error (RMSE) are the two most representative important indicators. In order to quantify the prediction effects of the three types of cloud computing, the attention corresponding to the four main contents of curriculum setting, teaching content, teaching scene setting, and teaching quality detection is used as the predictive index. Then through the nonlinear calculation process, the application effects of the three calculation methods are compared. The corresponding coefficient of R2 and RMSE are obtained as shown in Figures 8-9. In order to clearly express the calculation method of statistical variables, we can get the calculation method of root mean square difference.(11)RMSE=1n×∑i=1nyi−xi2.Figure 8
Predictive indicators corresponding to curriculum setting and teaching content. (a) Predicted performance for course offering content. (b) Predictive performance for instructional content.
(a)(b)Figure 9
Predictive indicators corresponding to teaching scene settings and teaching quality. (a) Predictive performance for setting content for instructional scenarios. (b) Prediction performance for teaching quality detection content.
(a)(b)It should be pointed out that FNSR stands for fuzzy neural reasoning system, ICSA stands for immune algorithm optimization, and ELMAN stands for Elman network.It can be seen from Figures8 to 9 that the coefficient of determination and the root mean square difference show a negative correlation. That is, a larger coefficient of determination corresponds to a smaller RMSE, and vice versa. For the four main contents of higher education, the coefficient of determination corresponding to ICSA is the closest to 1, and the root mean square difference is the smallest. So its prediction performance is better.As shown in Figure10, we use the ICSA algorithm to predict the causality of the two main contents of teaching scene setting and teaching quality detection. And a scatter plot can be drawn as follows. As shown in Figure 10, the corresponding value of the cause degree of the teaching scene setting and the teaching quality detection is approximately a polynomial function. The polynomial function relationship corresponding to the cause degree of teaching scene setting and teaching quality detection can be expressed as follows.(12)y2=1.76x2−0.01x+0.224.Figure 10
Mathematical connection between the two main contents of higher education.


## 5. Conclusion

The development of higher education is inseparable from the support of new information technology, so the impact of theera of big data on higher education is multi-faceted. The innovation model based on big data has also accelerated the reform process of higher education. This study studies the application effect of different algorithms or technologies in the reform of higher education by studying the intelligent algorithm part of the cloud computing program. Three typical intelligent algorithms are introduced to conduct predictive research on the four main aspects involved in higher education reform. The results show that the prediction effect based on the immune cloning algorithm is the best. In addition, through the method of mathematical fitting calculation, it can be found that the causal relationship between the two main contents of teaching scene setting and teaching quality detection approximately presents a polynomial function relationship.



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