# Numerical Analysis and Scientific Calculation Considering the Management Mechanism of College Students’ Innovation and Entrepreneurship Education

Authors: Sheng Wang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Purpose. This article aims to study how to analyze and study the numerical value of education management mechanisms based on cloud computing and describe the innovation and entrepreneurship of college students. Methodology. This article addresses the problems of numerical analysis and scientific computing. This problem is based on cloud computing, so it elaborates on the concepts and related algorithms of cloud computing and big data and designs and analyzes cases of numerical analysis and scientific computing of educational management mechanisms. Research Findings. Through the research of different kernel functions, the IG_CDmRMR algorithm can obtain relatively high accuracy results for numerical analysis and scientific computing. The IG_CDmRMR algorithm is the closest to expert evaluation. The maximum difference is 0.002, which is consistent in sample three. The maximum difference of the IG algorithm is 0.005, and the minimum difference is 0.002. The evaluation effect of the IG_CDmRMR algorithm is closer to the evaluation effect of experts. Practical Implications. It analyzes the numerical value of the education management mechanism and finds that the accuracy has a certain height. This has certain evaluation significance for the management mechanism of college students’ innovation and entrepreneurship education.


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## Body

## 1. Introduction

With the quick improvement of data innovation, information blast has turned into a conspicuous issue in numerous logical fields. Huge data provide rich information and broaden people’s horizons, but it also bring difficulties to data processing and storage. It is mainly manifested in the following aspects: there are a large number of heterogeneous data sources in different information systems. The data lack a unified and standardized organization method. In some fields, massive data exist in the form of a large number of small files, which are difficult to analyze and process effectively. In addition, it is also necessary to solve the problem of efficient storage of massive data. Later, the continuous maturity and development of cloud computing technology provides a new and effective method for processing massive data. With the progress of society and the development of the economy, the educational undertaking continues to progress and develop. The original educational model of colleges and universities can no longer adapt to today’s fast-developing society, and it is necessary to innovate and develop the mechanism of college management.The key to the healthy development of innovation and entrepreneurship education in colleges and universities lies in improving the overall quality of students and their social status of innovation ability. In the process of comprehensive reform and transformation and development, colleges and universities are facing common problems such as unreasonable allocation of resources, imperfect classification management and classification construction mechanism, unclear school orientation and discipline and professional setting, faculty and local service capabilities, and insufficient entrepreneurial ability of graduates. How to cultivate willing, pragmatic, capable, and brave entrepreneurial talents and how to closely combine employment with entrepreneurship are important ways to promote the transformation and development of colleges and universities. This requires that local colleges and universities must recognize the problems and development of innovation and entrepreneurship education, clarify goals, stimulate spirit, and innovate and develop. It is necessary to rationally improve the innovation and entrepreneurship education system and methods, and cultivate students from multiple perspectives.This article compares and analyzes the values of different kernel functions. Different evaluation results highlight the accuracy of the IG_CDmRMR algorithm. The innovations of this article are (1) this article combines cloud computing with innovation and entrepreneurship. It mainly introduces the K-nearest neighbor method, the naive Bayes classification method, and the support vector machine. (2) When faced with managing data, it uses kernel functions for numerical analysis and scientific calculations. By evaluating the experimental results and comparing the performance of different methods, it draws the advantages of the IG_CDmRMR algorithm.


## 2. Related Work

The solid advancement and business venture training in schools and colleges are of extraordinary importance to undergrads and even to public development, logical and innovative advancement, easing work pressure, and the endurance and improvement of schools and colleges. Zhu reviewed the basic situation of laboratory construction, practice base construction, and scientific training of innovation and entrepreneurship education in Chinese universities from 2010 to 2015 [1]. The purpose of Martynyshyn and Feicher was to identify and validate the prioritized subsystems that are functionally supported by the organizational and economic mechanisms of the management of Ukrainian art higher education institutions and to further develop alternative management solutions for their successful implementation [2]. Liang and Zeng conducted research on the long-term mechanism of socialist core values education in primary and secondary schools [3]. Under the foundation of the Internet, Cheng et al. examined the showing practice of the showing material subject political philosophy course from the parts of showing idea, showing configuration, showing interaction, and showing assessment system. This can actually work on understudies’ philosophical and political cooperation and fulfillment [4]. Kleschev studied and evaluated the current problems of the participation of state institutions of the Russian Federation in the implementation of priority state projects in the fields of science and education. He identified the factors that hinder the maximum effective functioning of state institutions in implementing measures within the framework of these projects [5]. However, the research on innovation and entrepreneurship education management of college students is not rich.The laws of inherent science are generally communicated by different numerical conditions. The motivation behind logical registering is to track down mathematical answers for these situations. Danaila et al. offered 12 computing projects. He aimed to numerically solve problems in a wide range of applications [6]. Based on this level of analysis, this study sorted out the security issues in global cyberspace and summarized the specific security problems in cyberspace [7]. Wei presented some recent advances in improving deep neural networks (DNNs) for scientific computing and classification applications [8]. West involved research work to understand how SARS-CoV-2 spreads in different populations, the biology and structure of the virus, and its infection mechanism [9]. However, the accuracy of the algorithm needs to be improved.


## 3. Management Methods of Scientific Computing

### 3.1. Cloud Computing and Big Data

#### 3.1.1. Cloud Computing

Cloud computing is the product of the integration of computer technologies and network technologies such as distributed computing, parallel computing, utility computing, network storage, virtualization, load balancing, and service-oriented architecture. Cloud computing is a new term born in the third quarter of 2007. But more than half a year later, its attention has surpassed grid computers, distributed computers, and parallel computers [10]. As shown in Figure 1, the number 100 represents the maximum search volume.Figure 1
Search trends of cloud computing, network computing, etc. in Google.The cloud computing model is a business computer model. It provides high-performance computers and mass storage through third-party network services. It shields the complexity of IT infrastructure and software platforms and automates management. It also provides high reliability, high scalability, and customizable and on-demand Web service functions [11]. Its characteristics are shown in Figure 2.Figure 2
Cloud computing features.Its research and implementation is a systematic project. It involves important issues such as data center management, resource virtualization, massive data processing, and computer security [12, 13], as shown in Figure 3.Figure 3
Links between cloud computing and related technologies.


#### 3.1.2. Big Data

Wikipedia’s definition of big data: big data refers to data sets that require more than the allowable time to acquire, using common software tools to manage and process the data [14]. International Data Corporation (IDC) believes that the 4V definitions of big data are volume, variety, velocity, and value.Generally speaking, big data service applications in different fields have different characteristics. Its users have different requirements for QoS indicators such as reliability and accuracy. Table1 lists some representative big data analysis services and their application characteristics.Table 1
Comparison of typical big data services.
The field of big data serviceUser concurrencyResponse time requirementsReliability requirementsAccuracy requirementsInternet of thingsBigQuickHighHighFinanceBigVery fastHighVery highWeb dataVery bigQuickHighHighMobile dataVery bigQuickHighHighMultimediaVery bigQuickHighModerateScientific computingSmallLowModerateVery highSocial networkVery bigQuickHighHighOn the other hand, cloud computing, as the technical support for big data processing, has created a series of cloud-based technologies and tools for processing big data. At present, the leading technologies and tools for processing big data are mainly Internet companies, which provide large-scale data storage and analysis services [15]. By combining enterprise data with external data for analysis, it helps enterprises obtain data value. Table 2 is a list of typical cloud-based big data tools.Table 2
List of typical cloud-based big data tools.
GoogleMicrosoftAmazonAli cloudData resource serviceGoogle Public Data ExplorerWindows Azure MarketplacePublic DatasetsData cubeBig data storage serviceGoogle cloud servicesAzureS3DataXMapReduceGoogle app engineHadoop on azureElastic MapReduce (EMR)LadderBig data analysis serviceBigQueryHadoop on azureEMRGarudaNoSQL databaseAppEngine datastoreTable storageDyamoDBOTSRelational databaseCloud SQLSQL azureMySQL & oracleMyfox & MySQLStreaming serviceSearch APIStreamInsightNoneGalaxyMachine learning servicesPrediction APIHadooptMahoutHadoop + MaboutODPS


### 3.2. Entrepreneurship Education Management in Colleges and Universities

According to statistics from China Education Online, since 2001, the number of Chinese university graduates has increased every year (Figure4). Difficulty in finding employment has become a difficult issue facing higher education reforms at present and for a long time in the future. At present, China is in a new era of extensive development of “mass entrepreneurship, mass innovation” and “Internet +.” Innovation and entrepreneurship education reform have become a new direction and pioneering training for colleges and universities to deepen the overall reform. Since 2016, colleges and universities across the country have organized innovation and entrepreneurship training courses, providing compulsory and optional training courses in innovation and entrepreneurship. This provides business guidance and assistance to students who wish to start a business and provides students with training in business management and other business practices [16, 17].Figure 4
The number of college graduates in China in 2001 and 2021 (10,000 people).Broadly speaking, innovation and entrepreneurship education is a creative educational practice activity. In a narrow sense, innovation and entrepreneurship education is a teaching activity that creates new employment opportunities. It is also a reform activity that helps them find their own career, flexible employment, and practical education for self-employment. With the help of “Innovation and Entrepreneurship” and “Internet +” Innovation and Entrepreneurship Competitions, incredible headway has been made in advancement and business instruction in Chinese colleges. Figure5(a) shows the current state of university management systems. At the same time, there are many disadvantages, as shown in Figure 5(b). The importance of innovation in higher education management is reflected in several aspects in Figure 5(c). To solve the problems of the insufficient total amount of students’ innovation and entrepreneurship education resources and low utilization rate, it is necessary to comprehensively manage the innovation and entrepreneurship education resources of colleges and universities. This is also a problem that needs to be solved in promoting the development of innovation and entrepreneurship education for college students and deepening the reform of university education. In the new situation, the innovation of the university management system should start from the three aspects shown in Figure 5(d).Figure 5
Brief introduction to innovation and entrepreneurship education in colleges and universities. (a) Current situation of management system in colleges and universities. (b) Problems that exist. (c) The importance of innovation in higher education management. (d) Innovation in the management system of colleges and universities under the new situation.
(a)(b)(c)(d)


### 3.3. Processing of University Education Management Files in Cloud Computing Environment

In a huge file system, frequently used and very frequently used files are mixed together. The lack of management tools makes it difficult for university administrators to distinguish, measure, and manage a large number of intermixed documents. To solve this problem, this chapter mainly designs a massive file processing model (C-MSFPM) based on Hadoop and MapReduce in the cloud computing environment. It deeply discusses and studies the related technologies in the C-MSFPM model design. The key technologies of the C-MSFPM model include document classification and cloud computing preprocessing strategies, document information indexing mechanisms, and document merging algorithms based on the principles of proximity and weight similarity [18, 19].


#### 3.3.1. Big Data Processing and Analysis Technology Based on Hadoop

As the most widely recognized big data technology implementation, Hadoop provides a parallel computing framework for processing large data. Key technologies include distributed HDFS file system and parallel MapReduce computing model.(1)
HDFS is a master-slave file system running on multiple nodes in a cluster. In HDFS, files are organized in file blocks (the default size is 64 KB). Each file block is stored on multiple nodes in the form of multiple backups (the default number of backups is 3), thus achieving high fault tolerance.As can be seen from the schematic diagram of the HDFS architecture in Figure6, the HDFS file system mainly includes three parts: the client, the master node, and the data node [20]. Among them, the master node is the manager of HDFS. It is responsible for managing the namespace of the file system, the configuration information of the cluster, and the replication information of data blocks. The data node is the actual storage location of the file. It stores data block information in the local file system and sends all data block information to the master node through periodic heartbeat messages, to facilitate the master node to manage files uniformly.(2)
MapReduce is an equal information handling model comprising two phases, Map and Reduce. In the Map stage, the information read from the circulated framework (like HDFS) is isolated into different bits of information as key-esteem matches. Hubs in the bunch that execute Map assignments will get key-esteem sets of information. The Map task autonomously processes the got key-esteem pair information and makes the middle of the road brings about the type of key-esteem matches. Middle-of-the-road results are saved money on the nearby circle of the hub executing the Map task. At the point when all Map undertakings are executed, the errands in the Reduce stage total the middle outcomes acquired by the assignments in light of similar fundamental qualities, and total the outcomes into the predefined document. The functional part of MapReduce is well suited for processing large data in a distributed parallel environment consisting of a large number of computers.Figure 6
Schematic diagram of HDFS architecture.


#### 3.3.2. File Class Division and Preprocessing Strategy in Cloud Environment

MapReduce is a programming model for data-intensive computers. Therefore, the best way to improve the efficiency of MapReduce applications is to divide the data and design the data model reasonably, to maximize the efficiency of the entire distributed computing. Its main goal is to efficiently and quickly process large text and graphic documents, such as PDF format documents and CAJ format documents. Initially, it performs the first classification according to the form of the document. Classified text documents are classified according to an improved KNN classification method based on MapReduce and feature vector reduction. It then combines small files of the same category into large files to reduce the waste of map resources caused by a large number of small files. Many small files here are mostly text files. To facilitate the batch operation of small files each year, the composition writes the small files directly to the large files in chronological order and then writes the name, replica, and content to the data node.For the classification technology of text files, scholars have proposed a variety of text classification technologies such as decision tree, neural network, KNN, Bayesian method, and support vector machine (SVM). In recent years, some new models or classification methods have been proposed in different applications. Some have achieved good results, such as the maximum entropy model and fuzzy theory. Here are some commonly used algorithms:(1) K-Nearest Neighbor Method. K-nearest neighbors (KNN) is a typical sorting algorithm proposed in 1968. Sorting and sorting algorithms can extract scores for documents belonging to multiple categories after a crisis. Presumably, the higher the score, the more likely it is to fall into this category. When KNN is used for classification, when the text for classification arrives, the similarity between it and each text in the training sample set should be calculated. It determines the most similar training texts and records the categories they belong to. On this basis, it scores each text class. The score is the sum of the similarity between the texts belonging to the class in the K training texts and the test texts, then it is sorted by score. At the same time, it chooses a limit, and only scores exceeding the limit will be considered.The specific algorithm steps are as follows:(1)
It performs necessary text preprocessing on all text in the training set to create feature vectors.(2)
To judge the text, the steps can be found in the method of step 1, and the text is represented by the feature vector.(3)
It calculates the similarity between the training samples’ K-nearest neighbors and the test samples, respectively. It findsK texts that are more similar to the text to be judged in the training set. The calculation formula is shown in (1).(1)Simqx,qb=cosqx,qb=∑a=1mwxawbx∑a=1mwxa2∑a=1mwxa2b=1,2,…,M.(4)
For theK neighborhoods of the text to be judged, it calculates the weight of each class. Formula (2) shows how the weights are calculated:(2)dx¯,cb=∑qa∈KNNSimx¯,qayq¯a,cb.Among them,x is the attribute vector similarity calculation formula of the text to be judged, which is Simx¯,qa, and the category attribute function is yq¯a,cb, namely:(3)q¯a=1,q¯∈cb,0,q¯∉cb.(5)
Comparing similarities and separating the texts to be judged on the most similar to them.(2) Naive Bayes Classification Method. Based on Bayes’ theorem, the point remains to first use the conditional probability formula to calculate the conditional probability of documents belonging to different classes. According to the principle of maximum probability, it sorts documents with maximum probability. Its assumption is that in a given text that looks like an environment, the distributions of text attributes are independent of each other [21].It assumes thatd is an arbitrary text, and it belongs to a category Sb of text S=S1,S2,…,SN. According to the classification of Naive Bayes, there are two kinds of formulas (4) and (5):(4)QSb|d=QSbQd|SbQd,(5)Qd=∑b=1nQSbQd|Sb.The above formula represents a given documentd. The probability that d belongs to class Sb. Therefore, the classification problem is transformed to compute the value QSb|d. Therefore, the class that receives the maximum value of QSb|dQSb|d is the class to which d belongs.It is the same for all categoriesQSb, then the sorting problem becomes a problem for Sb, as shown in formula (6).(6)QSb|d=maxa=1nQSb|d.(3) Support Vector Machines (SVM). The technique hypothesis of SVM depends on high-scope handling and relapse in measurements. Since text characterization errands as a rule use include words or N-gram results as component things, text highlight things will shape a huge number of text vectors. SVM maps text vector information into higher layered information space. It finds a hyperplane that can divide the data from the high-dimensional space, and maximizes the distance between this plane and the data as much as possible. The larger the distance, the better the classification effect will be.Linear support vector machines fall into two categories: linearly separable and linearly inseparable. A linearly separable SVM means that data can be directly segmented using a hyperplane. It assumes that there is a set of binary data distributed on the same two-dimensional plane. If there is a straight lineL that can completely separate the two types of data, then this straight line is called a hyperplane. For the convenience of calculation, the data labels on both sides of the hyperplane are usually set to y = +1 and y = −1. It expresses the above description as shown in Figure 7(a). The purple triangles and yellow five-pointed stars represent two types of data sets, respectively.Figure 7
SVM representation diagram. (a) Linear classification representation graph. (b) Linear classification representation graph in SVM. (c) Hyperplane interval description graph in SVM.
(a)(b)(c)The linearly separable problem in Figure7(a) is solved by the support vector machine, that is, by finding the function fx=wTx+r=0 as the hyperplane, the corresponding straight lines y = −1 and y = +1 can be expressed as:(7)wTx+r=−1,wTx+r=+1.The support vector machine to solve the linearly separable problem can be represented as shown in Figure7(b).For the convenience of representation, the hyperplane is defined asS1 in Figure 7(b), the planes on both sides of the hyperplane are defined as S2 and S3, respectively, and the distance between S2 and S3 is the classification interval. While there are multiple split planes between the S2 and S3 planes, there is only one plane that perfectly separates the data. The theoretical idea of the support vector machine is to find a segmentation plane in so many segmentation planes, so that the classification interval is the largest. This dividing plane is called the optimal dividing plane. In Figure 7(b), hyperplane S1 is the optimal dividing surface, which falls on the points on lines S2 and S3. Since it is closest to the optimal hyperplane S1, it is called a sample point. These points are also called support vectors because they are the main source of SVM calculations. The distance from the support vector to S1 is calculated as:(8)fxw=wTx+rw.It can also be represented as1/w, and the above classification interval representation is shown in Figure 7(c). It can be seen from Figure 7(c) that the distance between the support vectors can be expressed as 2/w.It assumes that the given training sample sets areC=x1,y1,x2,y2,…,xm,ym and ya∈−1,+1. To find the longest dividing hyperplane in space is to find the w and r parameters that satisfy the constraints, so that 2/w is the largest, which is shown in formula (9).(9)maxw,r2ws.t.yawTxa+r≥1,a=1,2,…,m.Obviously, whenw−1 is maximized, the interval can be maximized, and at the same time, it is equivalent to minimize w2, so formula (9) can be rewritten as formula (10).(10)maxw,r12w2s.t.yawTxa+r≥1,a=1,2,…,m.It treats formula (10) as a convex quadratic programming problem with constraint terms. Due to the particularity of the convex quadratic programming problem, to make the calculation more convenient, the Lagrange multiplier method of formula (10) can be used to obtain its “dual problem.” It adds Lagrange multipliers to βa≥0. The Lagrangian form of the problem can be expressed as formula (11).(11)Sw,r,β=12w2−∑a=1mβa1−yawTxa+r.To minimizeS-containing parameters w and r, the partial derivatives of S with respect to w and r can be set to 0, respectively. It can be obtained after calculation and transformation:(12)w=∑a=1mβayaxa,∑a=1mβaya=0.It takes the two formulas intoSw,r,β, yielding formula (13).(13)maxβ∑a=1mβaya−12∑a=1m∑a=1mβaβbyaybxaTxb,s.t.∑a=1mβaya=0,βa≥0,a=1,2,…,m.After solvingβ according to formula (14), both w and r can be obtained, and a classification model can also be obtained, as shown in formula (14).(14)fx=signwTx+r=sign∑a=1mβayaxaTx+r.This transforms the original problem into an equivalent dual problem. But this obviously makes the solution to the problem more convenient.In practical training tasks, the data is often difficult to be completely partitioned. One of the ways to solve this problem is to use “soft space.” It assumes that in the process of applying classification, some sample points may be allowed to satisfy constraintyawTxa+r≥1. At the same time, these samples that do not meet the constraints should be as few as possible, so formula (10) can be rewritten as formula (15).(15)maxw,r,δa12w2+D∑a=1mδa,s.t.yawTxa+r≥1−δaδa≥0,a=1,2,…,m.In formula (15), d represents the sample slack variable, which is used to represent the degree to which the sample does not satisfy the constraints. After adding slack variables, SVM is still a second-order programming problem, so the Lagrange multiplier method can also be used, which is expressed as formula (16).(16)S=w,r,β,δ,ϑ=12w2+D∑a=1mδa+∑a=1mβa1−δa−yawTxa+r−∑a=1mϑaδa.βa≥0, ϑa≥0 are expressed as Lagrange multipliers. It makes the partial derivatives S=w,r,β,δ,ϑ for w and b, respectively, which can be obtained after calculation:(17)w=∑a=1mβayaxa,∑a=1mβaya=0,D=βa+ϑa.Taking the three formulas intoS=w,r,β,δ,ϑ, we can get formula (18):(18)maxβ∑a=1mβa−12∑a=1m∑a=1mβaβbyaybxaTxb,s.t.∑a=1mβaya=0,0≤βa≤D,a=1,2,…,m.Comparing the soft-margin SVM of formula (18) with formula (15), it can be seen that the only difference between the two is that the constraints on the bivariate are different. Therefore, the same solution method can also be used for the modeling of soft-space support vector machines.Most of the data in actual production and life are nonlinear data, which cannot be directly classified using linear methods. To solve this problem, SVM can transform data from low-dimensional space to high-dimensional space through mapping. Although it transforms the problem into a high-dimensional space, the inner product operation of the kernel function neither complicate the problem, nor does it cause the problem of dimensional disaster [22]. However, the kernel function of the nonlinear mapping used here must satisfy the Mercer condition. When the support vector machine is added to the kernel function to solve, the Lagrange multiplier method can still be used as shown in formula (19).(19)maxβ∑a=1mβa−12∑a=1m∑a=1mβaβbyaybKxaxb,s.t.∑a=1mβaya=0,0≤βa≤D,a=1,2,…,m.Kxaxb represents the kernel function, and the final classification model function is formula (20).(20)fx=sign∑a=1mβayaKxa,x0+r.In the whole process of obtaining parameters, only the inner product between the two vectors is involved, and the solution to a specific form in the feature space is not required. Therefore, using the kernel functionKxaxb, the original space can be easily mapped to solve the nonlinear problem.


## 3.1. Cloud Computing and Big Data

### 3.1.1. Cloud Computing

Cloud computing is the product of the integration of computer technologies and network technologies such as distributed computing, parallel computing, utility computing, network storage, virtualization, load balancing, and service-oriented architecture. Cloud computing is a new term born in the third quarter of 2007. But more than half a year later, its attention has surpassed grid computers, distributed computers, and parallel computers [10]. As shown in Figure 1, the number 100 represents the maximum search volume.Figure 1
Search trends of cloud computing, network computing, etc. in Google.The cloud computing model is a business computer model. It provides high-performance computers and mass storage through third-party network services. It shields the complexity of IT infrastructure and software platforms and automates management. It also provides high reliability, high scalability, and customizable and on-demand Web service functions [11]. Its characteristics are shown in Figure 2.Figure 2
Cloud computing features.Its research and implementation is a systematic project. It involves important issues such as data center management, resource virtualization, massive data processing, and computer security [12, 13], as shown in Figure 3.Figure 3
Links between cloud computing and related technologies.


### 3.1.2. Big Data

Wikipedia’s definition of big data: big data refers to data sets that require more than the allowable time to acquire, using common software tools to manage and process the data [14]. International Data Corporation (IDC) believes that the 4V definitions of big data are volume, variety, velocity, and value.Generally speaking, big data service applications in different fields have different characteristics. Its users have different requirements for QoS indicators such as reliability and accuracy. Table1 lists some representative big data analysis services and their application characteristics.Table 1
Comparison of typical big data services.
The field of big data serviceUser concurrencyResponse time requirementsReliability requirementsAccuracy requirementsInternet of thingsBigQuickHighHighFinanceBigVery fastHighVery highWeb dataVery bigQuickHighHighMobile dataVery bigQuickHighHighMultimediaVery bigQuickHighModerateScientific computingSmallLowModerateVery highSocial networkVery bigQuickHighHighOn the other hand, cloud computing, as the technical support for big data processing, has created a series of cloud-based technologies and tools for processing big data. At present, the leading technologies and tools for processing big data are mainly Internet companies, which provide large-scale data storage and analysis services [15]. By combining enterprise data with external data for analysis, it helps enterprises obtain data value. Table 2 is a list of typical cloud-based big data tools.Table 2
List of typical cloud-based big data tools.
GoogleMicrosoftAmazonAli cloudData resource serviceGoogle Public Data ExplorerWindows Azure MarketplacePublic DatasetsData cubeBig data storage serviceGoogle cloud servicesAzureS3DataXMapReduceGoogle app engineHadoop on azureElastic MapReduce (EMR)LadderBig data analysis serviceBigQueryHadoop on azureEMRGarudaNoSQL databaseAppEngine datastoreTable storageDyamoDBOTSRelational databaseCloud SQLSQL azureMySQL & oracleMyfox & MySQLStreaming serviceSearch APIStreamInsightNoneGalaxyMachine learning servicesPrediction APIHadooptMahoutHadoop + MaboutODPS


## 3.1.1. Cloud Computing

Cloud computing is the product of the integration of computer technologies and network technologies such as distributed computing, parallel computing, utility computing, network storage, virtualization, load balancing, and service-oriented architecture. Cloud computing is a new term born in the third quarter of 2007. But more than half a year later, its attention has surpassed grid computers, distributed computers, and parallel computers [10]. As shown in Figure 1, the number 100 represents the maximum search volume.Figure 1
Search trends of cloud computing, network computing, etc. in Google.The cloud computing model is a business computer model. It provides high-performance computers and mass storage through third-party network services. It shields the complexity of IT infrastructure and software platforms and automates management. It also provides high reliability, high scalability, and customizable and on-demand Web service functions [11]. Its characteristics are shown in Figure 2.Figure 2
Cloud computing features.Its research and implementation is a systematic project. It involves important issues such as data center management, resource virtualization, massive data processing, and computer security [12, 13], as shown in Figure 3.Figure 3
Links between cloud computing and related technologies.


## 3.1.2. Big Data

Wikipedia’s definition of big data: big data refers to data sets that require more than the allowable time to acquire, using common software tools to manage and process the data [14]. International Data Corporation (IDC) believes that the 4V definitions of big data are volume, variety, velocity, and value.Generally speaking, big data service applications in different fields have different characteristics. Its users have different requirements for QoS indicators such as reliability and accuracy. Table1 lists some representative big data analysis services and their application characteristics.Table 1
Comparison of typical big data services.
The field of big data serviceUser concurrencyResponse time requirementsReliability requirementsAccuracy requirementsInternet of thingsBigQuickHighHighFinanceBigVery fastHighVery highWeb dataVery bigQuickHighHighMobile dataVery bigQuickHighHighMultimediaVery bigQuickHighModerateScientific computingSmallLowModerateVery highSocial networkVery bigQuickHighHighOn the other hand, cloud computing, as the technical support for big data processing, has created a series of cloud-based technologies and tools for processing big data. At present, the leading technologies and tools for processing big data are mainly Internet companies, which provide large-scale data storage and analysis services [15]. By combining enterprise data with external data for analysis, it helps enterprises obtain data value. Table 2 is a list of typical cloud-based big data tools.Table 2
List of typical cloud-based big data tools.
GoogleMicrosoftAmazonAli cloudData resource serviceGoogle Public Data ExplorerWindows Azure MarketplacePublic DatasetsData cubeBig data storage serviceGoogle cloud servicesAzureS3DataXMapReduceGoogle app engineHadoop on azureElastic MapReduce (EMR)LadderBig data analysis serviceBigQueryHadoop on azureEMRGarudaNoSQL databaseAppEngine datastoreTable storageDyamoDBOTSRelational databaseCloud SQLSQL azureMySQL & oracleMyfox & MySQLStreaming serviceSearch APIStreamInsightNoneGalaxyMachine learning servicesPrediction APIHadooptMahoutHadoop + MaboutODPS


## 3.2. Entrepreneurship Education Management in Colleges and Universities

According to statistics from China Education Online, since 2001, the number of Chinese university graduates has increased every year (Figure4). Difficulty in finding employment has become a difficult issue facing higher education reforms at present and for a long time in the future. At present, China is in a new era of extensive development of “mass entrepreneurship, mass innovation” and “Internet +.” Innovation and entrepreneurship education reform have become a new direction and pioneering training for colleges and universities to deepen the overall reform. Since 2016, colleges and universities across the country have organized innovation and entrepreneurship training courses, providing compulsory and optional training courses in innovation and entrepreneurship. This provides business guidance and assistance to students who wish to start a business and provides students with training in business management and other business practices [16, 17].Figure 4
The number of college graduates in China in 2001 and 2021 (10,000 people).Broadly speaking, innovation and entrepreneurship education is a creative educational practice activity. In a narrow sense, innovation and entrepreneurship education is a teaching activity that creates new employment opportunities. It is also a reform activity that helps them find their own career, flexible employment, and practical education for self-employment. With the help of “Innovation and Entrepreneurship” and “Internet +” Innovation and Entrepreneurship Competitions, incredible headway has been made in advancement and business instruction in Chinese colleges. Figure5(a) shows the current state of university management systems. At the same time, there are many disadvantages, as shown in Figure 5(b). The importance of innovation in higher education management is reflected in several aspects in Figure 5(c). To solve the problems of the insufficient total amount of students’ innovation and entrepreneurship education resources and low utilization rate, it is necessary to comprehensively manage the innovation and entrepreneurship education resources of colleges and universities. This is also a problem that needs to be solved in promoting the development of innovation and entrepreneurship education for college students and deepening the reform of university education. In the new situation, the innovation of the university management system should start from the three aspects shown in Figure 5(d).Figure 5
Brief introduction to innovation and entrepreneurship education in colleges and universities. (a) Current situation of management system in colleges and universities. (b) Problems that exist. (c) The importance of innovation in higher education management. (d) Innovation in the management system of colleges and universities under the new situation.
(a)(b)(c)(d)


## 3.3. Processing of University Education Management Files in Cloud Computing Environment

In a huge file system, frequently used and very frequently used files are mixed together. The lack of management tools makes it difficult for university administrators to distinguish, measure, and manage a large number of intermixed documents. To solve this problem, this chapter mainly designs a massive file processing model (C-MSFPM) based on Hadoop and MapReduce in the cloud computing environment. It deeply discusses and studies the related technologies in the C-MSFPM model design. The key technologies of the C-MSFPM model include document classification and cloud computing preprocessing strategies, document information indexing mechanisms, and document merging algorithms based on the principles of proximity and weight similarity [18, 19].


### 3.3.1. Big Data Processing and Analysis Technology Based on Hadoop

As the most widely recognized big data technology implementation, Hadoop provides a parallel computing framework for processing large data. Key technologies include distributed HDFS file system and parallel MapReduce computing model.(1)
HDFS is a master-slave file system running on multiple nodes in a cluster. In HDFS, files are organized in file blocks (the default size is 64 KB). Each file block is stored on multiple nodes in the form of multiple backups (the default number of backups is 3), thus achieving high fault tolerance.As can be seen from the schematic diagram of the HDFS architecture in Figure6, the HDFS file system mainly includes three parts: the client, the master node, and the data node [20]. Among them, the master node is the manager of HDFS. It is responsible for managing the namespace of the file system, the configuration information of the cluster, and the replication information of data blocks. The data node is the actual storage location of the file. It stores data block information in the local file system and sends all data block information to the master node through periodic heartbeat messages, to facilitate the master node to manage files uniformly.(2)
MapReduce is an equal information handling model comprising two phases, Map and Reduce. In the Map stage, the information read from the circulated framework (like HDFS) is isolated into different bits of information as key-esteem matches. Hubs in the bunch that execute Map assignments will get key-esteem sets of information. The Map task autonomously processes the got key-esteem pair information and makes the middle of the road brings about the type of key-esteem matches. Middle-of-the-road results are saved money on the nearby circle of the hub executing the Map task. At the point when all Map undertakings are executed, the errands in the Reduce stage total the middle outcomes acquired by the assignments in light of similar fundamental qualities, and total the outcomes into the predefined document. The functional part of MapReduce is well suited for processing large data in a distributed parallel environment consisting of a large number of computers.Figure 6
Schematic diagram of HDFS architecture.


### 3.3.2. File Class Division and Preprocessing Strategy in Cloud Environment

MapReduce is a programming model for data-intensive computers. Therefore, the best way to improve the efficiency of MapReduce applications is to divide the data and design the data model reasonably, to maximize the efficiency of the entire distributed computing. Its main goal is to efficiently and quickly process large text and graphic documents, such as PDF format documents and CAJ format documents. Initially, it performs the first classification according to the form of the document. Classified text documents are classified according to an improved KNN classification method based on MapReduce and feature vector reduction. It then combines small files of the same category into large files to reduce the waste of map resources caused by a large number of small files. Many small files here are mostly text files. To facilitate the batch operation of small files each year, the composition writes the small files directly to the large files in chronological order and then writes the name, replica, and content to the data node.For the classification technology of text files, scholars have proposed a variety of text classification technologies such as decision tree, neural network, KNN, Bayesian method, and support vector machine (SVM). In recent years, some new models or classification methods have been proposed in different applications. Some have achieved good results, such as the maximum entropy model and fuzzy theory. Here are some commonly used algorithms:(1) K-Nearest Neighbor Method. K-nearest neighbors (KNN) is a typical sorting algorithm proposed in 1968. Sorting and sorting algorithms can extract scores for documents belonging to multiple categories after a crisis. Presumably, the higher the score, the more likely it is to fall into this category. When KNN is used for classification, when the text for classification arrives, the similarity between it and each text in the training sample set should be calculated. It determines the most similar training texts and records the categories they belong to. On this basis, it scores each text class. The score is the sum of the similarity between the texts belonging to the class in the K training texts and the test texts, then it is sorted by score. At the same time, it chooses a limit, and only scores exceeding the limit will be considered.The specific algorithm steps are as follows:(1)
It performs necessary text preprocessing on all text in the training set to create feature vectors.(2)
To judge the text, the steps can be found in the method of step 1, and the text is represented by the feature vector.(3)
It calculates the similarity between the training samples’ K-nearest neighbors and the test samples, respectively. It findsK texts that are more similar to the text to be judged in the training set. The calculation formula is shown in (1).(1)Simqx,qb=cosqx,qb=∑a=1mwxawbx∑a=1mwxa2∑a=1mwxa2b=1,2,…,M.(4)
For theK neighborhoods of the text to be judged, it calculates the weight of each class. Formula (2) shows how the weights are calculated:(2)dx¯,cb=∑qa∈KNNSimx¯,qayq¯a,cb.Among them,x is the attribute vector similarity calculation formula of the text to be judged, which is Simx¯,qa, and the category attribute function is yq¯a,cb, namely:(3)q¯a=1,q¯∈cb,0,q¯∉cb.(5)
Comparing similarities and separating the texts to be judged on the most similar to them.(2) Naive Bayes Classification Method. Based on Bayes’ theorem, the point remains to first use the conditional probability formula to calculate the conditional probability of documents belonging to different classes. According to the principle of maximum probability, it sorts documents with maximum probability. Its assumption is that in a given text that looks like an environment, the distributions of text attributes are independent of each other [21].It assumes thatd is an arbitrary text, and it belongs to a category Sb of text S=S1,S2,…,SN. According to the classification of Naive Bayes, there are two kinds of formulas (4) and (5):(4)QSb|d=QSbQd|SbQd,(5)Qd=∑b=1nQSbQd|Sb.The above formula represents a given documentd. The probability that d belongs to class Sb. Therefore, the classification problem is transformed to compute the value QSb|d. Therefore, the class that receives the maximum value of QSb|dQSb|d is the class to which d belongs.It is the same for all categoriesQSb, then the sorting problem becomes a problem for Sb, as shown in formula (6).(6)QSb|d=maxa=1nQSb|d.(3) Support Vector Machines (SVM). The technique hypothesis of SVM depends on high-scope handling and relapse in measurements. Since text characterization errands as a rule use include words or N-gram results as component things, text highlight things will shape a huge number of text vectors. SVM maps text vector information into higher layered information space. It finds a hyperplane that can divide the data from the high-dimensional space, and maximizes the distance between this plane and the data as much as possible. The larger the distance, the better the classification effect will be.Linear support vector machines fall into two categories: linearly separable and linearly inseparable. A linearly separable SVM means that data can be directly segmented using a hyperplane. It assumes that there is a set of binary data distributed on the same two-dimensional plane. If there is a straight lineL that can completely separate the two types of data, then this straight line is called a hyperplane. For the convenience of calculation, the data labels on both sides of the hyperplane are usually set to y = +1 and y = −1. It expresses the above description as shown in Figure 7(a). The purple triangles and yellow five-pointed stars represent two types of data sets, respectively.Figure 7
SVM representation diagram. (a) Linear classification representation graph. (b) Linear classification representation graph in SVM. (c) Hyperplane interval description graph in SVM.
(a)(b)(c)The linearly separable problem in Figure7(a) is solved by the support vector machine, that is, by finding the function fx=wTx+r=0 as the hyperplane, the corresponding straight lines y = −1 and y = +1 can be expressed as:(7)wTx+r=−1,wTx+r=+1.The support vector machine to solve the linearly separable problem can be represented as shown in Figure7(b).For the convenience of representation, the hyperplane is defined asS1 in Figure 7(b), the planes on both sides of the hyperplane are defined as S2 and S3, respectively, and the distance between S2 and S3 is the classification interval. While there are multiple split planes between the S2 and S3 planes, there is only one plane that perfectly separates the data. The theoretical idea of the support vector machine is to find a segmentation plane in so many segmentation planes, so that the classification interval is the largest. This dividing plane is called the optimal dividing plane. In Figure 7(b), hyperplane S1 is the optimal dividing surface, which falls on the points on lines S2 and S3. Since it is closest to the optimal hyperplane S1, it is called a sample point. These points are also called support vectors because they are the main source of SVM calculations. The distance from the support vector to S1 is calculated as:(8)fxw=wTx+rw.It can also be represented as1/w, and the above classification interval representation is shown in Figure 7(c). It can be seen from Figure 7(c) that the distance between the support vectors can be expressed as 2/w.It assumes that the given training sample sets areC=x1,y1,x2,y2,…,xm,ym and ya∈−1,+1. To find the longest dividing hyperplane in space is to find the w and r parameters that satisfy the constraints, so that 2/w is the largest, which is shown in formula (9).(9)maxw,r2ws.t.yawTxa+r≥1,a=1,2,…,m.Obviously, whenw−1 is maximized, the interval can be maximized, and at the same time, it is equivalent to minimize w2, so formula (9) can be rewritten as formula (10).(10)maxw,r12w2s.t.yawTxa+r≥1,a=1,2,…,m.It treats formula (10) as a convex quadratic programming problem with constraint terms. Due to the particularity of the convex quadratic programming problem, to make the calculation more convenient, the Lagrange multiplier method of formula (10) can be used to obtain its “dual problem.” It adds Lagrange multipliers to βa≥0. The Lagrangian form of the problem can be expressed as formula (11).(11)Sw,r,β=12w2−∑a=1mβa1−yawTxa+r.To minimizeS-containing parameters w and r, the partial derivatives of S with respect to w and r can be set to 0, respectively. It can be obtained after calculation and transformation:(12)w=∑a=1mβayaxa,∑a=1mβaya=0.It takes the two formulas intoSw,r,β, yielding formula (13).(13)maxβ∑a=1mβaya−12∑a=1m∑a=1mβaβbyaybxaTxb,s.t.∑a=1mβaya=0,βa≥0,a=1,2,…,m.After solvingβ according to formula (14), both w and r can be obtained, and a classification model can also be obtained, as shown in formula (14).(14)fx=signwTx+r=sign∑a=1mβayaxaTx+r.This transforms the original problem into an equivalent dual problem. But this obviously makes the solution to the problem more convenient.In practical training tasks, the data is often difficult to be completely partitioned. One of the ways to solve this problem is to use “soft space.” It assumes that in the process of applying classification, some sample points may be allowed to satisfy constraintyawTxa+r≥1. At the same time, these samples that do not meet the constraints should be as few as possible, so formula (10) can be rewritten as formula (15).(15)maxw,r,δa12w2+D∑a=1mδa,s.t.yawTxa+r≥1−δaδa≥0,a=1,2,…,m.In formula (15), d represents the sample slack variable, which is used to represent the degree to which the sample does not satisfy the constraints. After adding slack variables, SVM is still a second-order programming problem, so the Lagrange multiplier method can also be used, which is expressed as formula (16).(16)S=w,r,β,δ,ϑ=12w2+D∑a=1mδa+∑a=1mβa1−δa−yawTxa+r−∑a=1mϑaδa.βa≥0, ϑa≥0 are expressed as Lagrange multipliers. It makes the partial derivatives S=w,r,β,δ,ϑ for w and b, respectively, which can be obtained after calculation:(17)w=∑a=1mβayaxa,∑a=1mβaya=0,D=βa+ϑa.Taking the three formulas intoS=w,r,β,δ,ϑ, we can get formula (18):(18)maxβ∑a=1mβa−12∑a=1m∑a=1mβaβbyaybxaTxb,s.t.∑a=1mβaya=0,0≤βa≤D,a=1,2,…,m.Comparing the soft-margin SVM of formula (18) with formula (15), it can be seen that the only difference between the two is that the constraints on the bivariate are different. Therefore, the same solution method can also be used for the modeling of soft-space support vector machines.Most of the data in actual production and life are nonlinear data, which cannot be directly classified using linear methods. To solve this problem, SVM can transform data from low-dimensional space to high-dimensional space through mapping. Although it transforms the problem into a high-dimensional space, the inner product operation of the kernel function neither complicate the problem, nor does it cause the problem of dimensional disaster [22]. However, the kernel function of the nonlinear mapping used here must satisfy the Mercer condition. When the support vector machine is added to the kernel function to solve, the Lagrange multiplier method can still be used as shown in formula (19).(19)maxβ∑a=1mβa−12∑a=1m∑a=1mβaβbyaybKxaxb,s.t.∑a=1mβaya=0,0≤βa≤D,a=1,2,…,m.Kxaxb represents the kernel function, and the final classification model function is formula (20).(20)fx=sign∑a=1mβayaKxa,x0+r.In the whole process of obtaining parameters, only the inner product between the two vectors is involved, and the solution to a specific form in the feature space is not required. Therefore, using the kernel functionKxaxb, the original space can be easily mapped to solve the nonlinear problem.


## 3.3.1. Big Data Processing and Analysis Technology Based on Hadoop

As the most widely recognized big data technology implementation, Hadoop provides a parallel computing framework for processing large data. Key technologies include distributed HDFS file system and parallel MapReduce computing model.(1)
HDFS is a master-slave file system running on multiple nodes in a cluster. In HDFS, files are organized in file blocks (the default size is 64 KB). Each file block is stored on multiple nodes in the form of multiple backups (the default number of backups is 3), thus achieving high fault tolerance.As can be seen from the schematic diagram of the HDFS architecture in Figure6, the HDFS file system mainly includes three parts: the client, the master node, and the data node [20]. Among them, the master node is the manager of HDFS. It is responsible for managing the namespace of the file system, the configuration information of the cluster, and the replication information of data blocks. The data node is the actual storage location of the file. It stores data block information in the local file system and sends all data block information to the master node through periodic heartbeat messages, to facilitate the master node to manage files uniformly.(2)
MapReduce is an equal information handling model comprising two phases, Map and Reduce. In the Map stage, the information read from the circulated framework (like HDFS) is isolated into different bits of information as key-esteem matches. Hubs in the bunch that execute Map assignments will get key-esteem sets of information. The Map task autonomously processes the got key-esteem pair information and makes the middle of the road brings about the type of key-esteem matches. Middle-of-the-road results are saved money on the nearby circle of the hub executing the Map task. At the point when all Map undertakings are executed, the errands in the Reduce stage total the middle outcomes acquired by the assignments in light of similar fundamental qualities, and total the outcomes into the predefined document. The functional part of MapReduce is well suited for processing large data in a distributed parallel environment consisting of a large number of computers.Figure 6
Schematic diagram of HDFS architecture.


## 3.3.2. File Class Division and Preprocessing Strategy in Cloud Environment

MapReduce is a programming model for data-intensive computers. Therefore, the best way to improve the efficiency of MapReduce applications is to divide the data and design the data model reasonably, to maximize the efficiency of the entire distributed computing. Its main goal is to efficiently and quickly process large text and graphic documents, such as PDF format documents and CAJ format documents. Initially, it performs the first classification according to the form of the document. Classified text documents are classified according to an improved KNN classification method based on MapReduce and feature vector reduction. It then combines small files of the same category into large files to reduce the waste of map resources caused by a large number of small files. Many small files here are mostly text files. To facilitate the batch operation of small files each year, the composition writes the small files directly to the large files in chronological order and then writes the name, replica, and content to the data node.For the classification technology of text files, scholars have proposed a variety of text classification technologies such as decision tree, neural network, KNN, Bayesian method, and support vector machine (SVM). In recent years, some new models or classification methods have been proposed in different applications. Some have achieved good results, such as the maximum entropy model and fuzzy theory. Here are some commonly used algorithms:(1) K-Nearest Neighbor Method. K-nearest neighbors (KNN) is a typical sorting algorithm proposed in 1968. Sorting and sorting algorithms can extract scores for documents belonging to multiple categories after a crisis. Presumably, the higher the score, the more likely it is to fall into this category. When KNN is used for classification, when the text for classification arrives, the similarity between it and each text in the training sample set should be calculated. It determines the most similar training texts and records the categories they belong to. On this basis, it scores each text class. The score is the sum of the similarity between the texts belonging to the class in the K training texts and the test texts, then it is sorted by score. At the same time, it chooses a limit, and only scores exceeding the limit will be considered.The specific algorithm steps are as follows:(1)
It performs necessary text preprocessing on all text in the training set to create feature vectors.(2)
To judge the text, the steps can be found in the method of step 1, and the text is represented by the feature vector.(3)
It calculates the similarity between the training samples’ K-nearest neighbors and the test samples, respectively. It findsK texts that are more similar to the text to be judged in the training set. The calculation formula is shown in (1).(1)Simqx,qb=cosqx,qb=∑a=1mwxawbx∑a=1mwxa2∑a=1mwxa2b=1,2,…,M.(4)
For theK neighborhoods of the text to be judged, it calculates the weight of each class. Formula (2) shows how the weights are calculated:(2)dx¯,cb=∑qa∈KNNSimx¯,qayq¯a,cb.Among them,x is the attribute vector similarity calculation formula of the text to be judged, which is Simx¯,qa, and the category attribute function is yq¯a,cb, namely:(3)q¯a=1,q¯∈cb,0,q¯∉cb.(5)
Comparing similarities and separating the texts to be judged on the most similar to them.(2) Naive Bayes Classification Method. Based on Bayes’ theorem, the point remains to first use the conditional probability formula to calculate the conditional probability of documents belonging to different classes. According to the principle of maximum probability, it sorts documents with maximum probability. Its assumption is that in a given text that looks like an environment, the distributions of text attributes are independent of each other [21].It assumes thatd is an arbitrary text, and it belongs to a category Sb of text S=S1,S2,…,SN. According to the classification of Naive Bayes, there are two kinds of formulas (4) and (5):(4)QSb|d=QSbQd|SbQd,(5)Qd=∑b=1nQSbQd|Sb.The above formula represents a given documentd. The probability that d belongs to class Sb. Therefore, the classification problem is transformed to compute the value QSb|d. Therefore, the class that receives the maximum value of QSb|dQSb|d is the class to which d belongs.It is the same for all categoriesQSb, then the sorting problem becomes a problem for Sb, as shown in formula (6).(6)QSb|d=maxa=1nQSb|d.(3) Support Vector Machines (SVM). The technique hypothesis of SVM depends on high-scope handling and relapse in measurements. Since text characterization errands as a rule use include words or N-gram results as component things, text highlight things will shape a huge number of text vectors. SVM maps text vector information into higher layered information space. It finds a hyperplane that can divide the data from the high-dimensional space, and maximizes the distance between this plane and the data as much as possible. The larger the distance, the better the classification effect will be.Linear support vector machines fall into two categories: linearly separable and linearly inseparable. A linearly separable SVM means that data can be directly segmented using a hyperplane. It assumes that there is a set of binary data distributed on the same two-dimensional plane. If there is a straight lineL that can completely separate the two types of data, then this straight line is called a hyperplane. For the convenience of calculation, the data labels on both sides of the hyperplane are usually set to y = +1 and y = −1. It expresses the above description as shown in Figure 7(a). The purple triangles and yellow five-pointed stars represent two types of data sets, respectively.Figure 7
SVM representation diagram. (a) Linear classification representation graph. (b) Linear classification representation graph in SVM. (c) Hyperplane interval description graph in SVM.
(a)(b)(c)The linearly separable problem in Figure7(a) is solved by the support vector machine, that is, by finding the function fx=wTx+r=0 as the hyperplane, the corresponding straight lines y = −1 and y = +1 can be expressed as:(7)wTx+r=−1,wTx+r=+1.The support vector machine to solve the linearly separable problem can be represented as shown in Figure7(b).For the convenience of representation, the hyperplane is defined asS1 in Figure 7(b), the planes on both sides of the hyperplane are defined as S2 and S3, respectively, and the distance between S2 and S3 is the classification interval. While there are multiple split planes between the S2 and S3 planes, there is only one plane that perfectly separates the data. The theoretical idea of the support vector machine is to find a segmentation plane in so many segmentation planes, so that the classification interval is the largest. This dividing plane is called the optimal dividing plane. In Figure 7(b), hyperplane S1 is the optimal dividing surface, which falls on the points on lines S2 and S3. Since it is closest to the optimal hyperplane S1, it is called a sample point. These points are also called support vectors because they are the main source of SVM calculations. The distance from the support vector to S1 is calculated as:(8)fxw=wTx+rw.It can also be represented as1/w, and the above classification interval representation is shown in Figure 7(c). It can be seen from Figure 7(c) that the distance between the support vectors can be expressed as 2/w.It assumes that the given training sample sets areC=x1,y1,x2,y2,…,xm,ym and ya∈−1,+1. To find the longest dividing hyperplane in space is to find the w and r parameters that satisfy the constraints, so that 2/w is the largest, which is shown in formula (9).(9)maxw,r2ws.t.yawTxa+r≥1,a=1,2,…,m.Obviously, whenw−1 is maximized, the interval can be maximized, and at the same time, it is equivalent to minimize w2, so formula (9) can be rewritten as formula (10).(10)maxw,r12w2s.t.yawTxa+r≥1,a=1,2,…,m.It treats formula (10) as a convex quadratic programming problem with constraint terms. Due to the particularity of the convex quadratic programming problem, to make the calculation more convenient, the Lagrange multiplier method of formula (10) can be used to obtain its “dual problem.” It adds Lagrange multipliers to βa≥0. The Lagrangian form of the problem can be expressed as formula (11).(11)Sw,r,β=12w2−∑a=1mβa1−yawTxa+r.To minimizeS-containing parameters w and r, the partial derivatives of S with respect to w and r can be set to 0, respectively. It can be obtained after calculation and transformation:(12)w=∑a=1mβayaxa,∑a=1mβaya=0.It takes the two formulas intoSw,r,β, yielding formula (13).(13)maxβ∑a=1mβaya−12∑a=1m∑a=1mβaβbyaybxaTxb,s.t.∑a=1mβaya=0,βa≥0,a=1,2,…,m.After solvingβ according to formula (14), both w and r can be obtained, and a classification model can also be obtained, as shown in formula (14).(14)fx=signwTx+r=sign∑a=1mβayaxaTx+r.This transforms the original problem into an equivalent dual problem. But this obviously makes the solution to the problem more convenient.In practical training tasks, the data is often difficult to be completely partitioned. One of the ways to solve this problem is to use “soft space.” It assumes that in the process of applying classification, some sample points may be allowed to satisfy constraintyawTxa+r≥1. At the same time, these samples that do not meet the constraints should be as few as possible, so formula (10) can be rewritten as formula (15).(15)maxw,r,δa12w2+D∑a=1mδa,s.t.yawTxa+r≥1−δaδa≥0,a=1,2,…,m.In formula (15), d represents the sample slack variable, which is used to represent the degree to which the sample does not satisfy the constraints. After adding slack variables, SVM is still a second-order programming problem, so the Lagrange multiplier method can also be used, which is expressed as formula (16).(16)S=w,r,β,δ,ϑ=12w2+D∑a=1mδa+∑a=1mβa1−δa−yawTxa+r−∑a=1mϑaδa.βa≥0, ϑa≥0 are expressed as Lagrange multipliers. It makes the partial derivatives S=w,r,β,δ,ϑ for w and b, respectively, which can be obtained after calculation:(17)w=∑a=1mβayaxa,∑a=1mβaya=0,D=βa+ϑa.Taking the three formulas intoS=w,r,β,δ,ϑ, we can get formula (18):(18)maxβ∑a=1mβa−12∑a=1m∑a=1mβaβbyaybxaTxb,s.t.∑a=1mβaya=0,0≤βa≤D,a=1,2,…,m.Comparing the soft-margin SVM of formula (18) with formula (15), it can be seen that the only difference between the two is that the constraints on the bivariate are different. Therefore, the same solution method can also be used for the modeling of soft-space support vector machines.Most of the data in actual production and life are nonlinear data, which cannot be directly classified using linear methods. To solve this problem, SVM can transform data from low-dimensional space to high-dimensional space through mapping. Although it transforms the problem into a high-dimensional space, the inner product operation of the kernel function neither complicate the problem, nor does it cause the problem of dimensional disaster [22]. However, the kernel function of the nonlinear mapping used here must satisfy the Mercer condition. When the support vector machine is added to the kernel function to solve, the Lagrange multiplier method can still be used as shown in formula (19).(19)maxβ∑a=1mβa−12∑a=1m∑a=1mβaβbyaybKxaxb,s.t.∑a=1mβaya=0,0≤βa≤D,a=1,2,…,m.Kxaxb represents the kernel function, and the final classification model function is formula (20).(20)fx=sign∑a=1mβayaKxa,x0+r.In the whole process of obtaining parameters, only the inner product between the two vectors is involved, and the solution to a specific form in the feature space is not required. Therefore, using the kernel functionKxaxb, the original space can be easily mapped to solve the nonlinear problem.


## 4. Numerical Analysis and Scientific Computing Experiment and Analysis of Educational Management Mechanism

### 4.1. Improved Numerical Algorithm for Mixed Functions

To guarantee the normalization of the information, the experiments in this chapter are preprocessed to remove stop words, punctuation marks, and other special characters. In the experiment, the TF-IDF method is used to vectorize the preprocessed corpus. It calculates the weight of each word in the text and normalizes it.The experiments in this chapter randomly select 60% of the management data as the training set and 40% as the test set. The selection of some parameters in the experiment adopts the grid search method. These include the penalty parameterS ∈ (1, 100) in SVM with a stride of 10. In the mixed kernel function, the exponent of the polynomial kernel function is chosen to be d = 3. The kernel weight β ∈ (0, 1) in the mixed kernel function, and the step size is 0.1. The experimental platform uses Python3.6. At the same time, the experiment in this chapter adopts the 5-fold cross-validation method, and the F1 value is used as the evaluation standard of the test, as shown in formula (21).(21)F1=2×TP2×TP+FP+FN×100%.Among them, TP means predicting the positive class as the number of positive classes and TN means predicting the negative class as the number of negative classes. FP predicts negative classes as positive class numbers and FN predicts positive classes as negative class numbers.Experiment 1.
Comparison and analysis of the performance of various kernel functions.
In this experiment, 1000 management mechanisms were selected as the experimental corpus. For the determination of the weight in the mixed kernel function, since the experimental performance of the single-kernel Fourier kernel function is higher than that of the polynomial kernel function, the weight coefficientβ is set to 0.25 in this experiment. The experimental results are shown in Figure 8.
Through the analysis of Figure8, it can be seen that with the increase of feature dimension, the final classification effect of all kernel functions is improved. The performance of the Fourier mixture kernel function is better than that of the single kernel function and the Gaussian kernel function and the polynomial kernel function. This also confirms the correctness of the idea of the combined kernel function and the superiority of the Fourier kernel function. It has certain guiding significance for the improvement of the classification effect of management documents in colleges and universities.Figure 8
Comparison ofF1 values of different kernel functions.Experiment 2.
Combined with the IG_CDmRMR algorithm to analyze the performance of various kernel functions.
In this experiment, the management mechanism is selected as the experimental corpus, combined with the IG_CDmRMR algorithm, to analyze the classification performance of various kernel functions SVM.
The experiments use IG and IG_CDmRMR algorithms as feature selection algorithms. It combines Gaussian kernel function, Fourier kernel function, and support vector machine of Fourier mixed kernel function with two feature selection methods to compare and analyze as shown in Figure9.
As shown in Figure9, it shows a trend of increasing first and then decreasing. When the feature dimension is 300–500, both feature selection algorithms show a better classification effect. With the increase in the number of features, the results of the two feature selection screening showed a significant downward trend. This shows that the number of words with weak representation ability in the feature subsets screened out by the feature is increasing, which interferes with the classification effect of the text.Figure 9
Performance analysis of multiple kernel functions based on IG and IG_CDmRMR feature selection.


### 4.2. Application Examples of Evaluation Management Mechanism

The cultivation of students’ innovative and entrepreneurial thinking and ability can be summarized according to the technical form of the Internet. The three categories of market thinking ability, user thinking ability, and product thinking ability can reflect the network thinking ability of college students. College innovation and entrepreneurship teachers choose appropriate teaching methods and methods to adapt to the particularity of students’ entrepreneurship. The “concentration storm” can also be used to force some successful Internet entrepreneurs into emerging teaching and discussions on how to start an internet business. Furthermore, the cultivation of network thinking and innovation ability should include all aspects of vocational education and personnel training, so that students can form a good network thinking ability. Finally, students also perceive a transfer of thinking skills and put network thinking and innovation into practice. Through real entrepreneurial activities, drafting entrepreneurial plans, and participating in entrepreneurial competitions, it strengthens students’ network thinking and innovation capabilities and improves entrepreneurial success rates.This time, five experts were selected to evaluate the management mechanism, and the IG and IG_CDmRMR algorithms were used for evaluation (Table3).Table 3
Evaluation results.
Evaluation methodSample 1Sample 2Sample 3Sample 4Sample 5Expert assessment0.7300.7790.8340.8960.946IG assessment0.7250.7760.8310.8940.944IG_CDmRMR evaluation0.7310.7810.8340.8970.946


## 4.1. Improved Numerical Algorithm for Mixed Functions

To guarantee the normalization of the information, the experiments in this chapter are preprocessed to remove stop words, punctuation marks, and other special characters. In the experiment, the TF-IDF method is used to vectorize the preprocessed corpus. It calculates the weight of each word in the text and normalizes it.The experiments in this chapter randomly select 60% of the management data as the training set and 40% as the test set. The selection of some parameters in the experiment adopts the grid search method. These include the penalty parameterS ∈ (1, 100) in SVM with a stride of 10. In the mixed kernel function, the exponent of the polynomial kernel function is chosen to be d = 3. The kernel weight β ∈ (0, 1) in the mixed kernel function, and the step size is 0.1. The experimental platform uses Python3.6. At the same time, the experiment in this chapter adopts the 5-fold cross-validation method, and the F1 value is used as the evaluation standard of the test, as shown in formula (21).(21)F1=2×TP2×TP+FP+FN×100%.Among them, TP means predicting the positive class as the number of positive classes and TN means predicting the negative class as the number of negative classes. FP predicts negative classes as positive class numbers and FN predicts positive classes as negative class numbers.Experiment 1.
Comparison and analysis of the performance of various kernel functions.
In this experiment, 1000 management mechanisms were selected as the experimental corpus. For the determination of the weight in the mixed kernel function, since the experimental performance of the single-kernel Fourier kernel function is higher than that of the polynomial kernel function, the weight coefficientβ is set to 0.25 in this experiment. The experimental results are shown in Figure 8.
Through the analysis of Figure8, it can be seen that with the increase of feature dimension, the final classification effect of all kernel functions is improved. The performance of the Fourier mixture kernel function is better than that of the single kernel function and the Gaussian kernel function and the polynomial kernel function. This also confirms the correctness of the idea of the combined kernel function and the superiority of the Fourier kernel function. It has certain guiding significance for the improvement of the classification effect of management documents in colleges and universities.Figure 8
Comparison ofF1 values of different kernel functions.Experiment 2.
Combined with the IG_CDmRMR algorithm to analyze the performance of various kernel functions.
In this experiment, the management mechanism is selected as the experimental corpus, combined with the IG_CDmRMR algorithm, to analyze the classification performance of various kernel functions SVM.
The experiments use IG and IG_CDmRMR algorithms as feature selection algorithms. It combines Gaussian kernel function, Fourier kernel function, and support vector machine of Fourier mixed kernel function with two feature selection methods to compare and analyze as shown in Figure9.
As shown in Figure9, it shows a trend of increasing first and then decreasing. When the feature dimension is 300–500, both feature selection algorithms show a better classification effect. With the increase in the number of features, the results of the two feature selection screening showed a significant downward trend. This shows that the number of words with weak representation ability in the feature subsets screened out by the feature is increasing, which interferes with the classification effect of the text.Figure 9
Performance analysis of multiple kernel functions based on IG and IG_CDmRMR feature selection.


## 4.2. Application Examples of Evaluation Management Mechanism

The cultivation of students’ innovative and entrepreneurial thinking and ability can be summarized according to the technical form of the Internet. The three categories of market thinking ability, user thinking ability, and product thinking ability can reflect the network thinking ability of college students. College innovation and entrepreneurship teachers choose appropriate teaching methods and methods to adapt to the particularity of students’ entrepreneurship. The “concentration storm” can also be used to force some successful Internet entrepreneurs into emerging teaching and discussions on how to start an internet business. Furthermore, the cultivation of network thinking and innovation ability should include all aspects of vocational education and personnel training, so that students can form a good network thinking ability. Finally, students also perceive a transfer of thinking skills and put network thinking and innovation into practice. Through real entrepreneurial activities, drafting entrepreneurial plans, and participating in entrepreneurial competitions, it strengthens students’ network thinking and innovation capabilities and improves entrepreneurial success rates.This time, five experts were selected to evaluate the management mechanism, and the IG and IG_CDmRMR algorithms were used for evaluation (Table3).Table 3
Evaluation results.
Evaluation methodSample 1Sample 2Sample 3Sample 4Sample 5Expert assessment0.7300.7790.8340.8960.946IG assessment0.7250.7760.8310.8940.944IG_CDmRMR evaluation0.7310.7810.8340.8970.946


## 5. Discussion

First, this article initially masters the basic knowledge related to cloud computing and analyzes how to conduct research on numerical analysis and scientific computing based on cloud computing. It expounds on the concept of innovation and entrepreneurship, studies theK-nearest neighbor method, explores the naive Bayesian classification method, and analyzes the applicability of the IG_CDmRMR algorithm in numerical analysis and scientific computing through experiments.As of now, understudy advancement and business venture instruction have turned into a significant measure for the country to carry out the development-driven improvement system, and it is likewise a forward leap for schools and colleges to extend training change. Lately, the quantity of school graduates has expanded decisively. The issue of troublesome work has turned into a strong main impetus for advancing the change of advancement and business schooling and advancing the business of graduates. The genuine idea of development and business venture schooling is to allow understudies completely to see the value in the appeal of advancement and business and really get familiar with the doable techniques for advancement and business venture.The experimental analysis in this article shows that compared with the IG feature selection method, no matter which kernel function is used in the SVM based on the IG_CDmRMR feature selection algorithm, the finalF1 value is still better than the IG algorithm as a whole. This also verifies the rationality of the IG_CDmRMR algorithm. The algorithm combination of Fourier mixed kernel function and IG_CDmRMR is 1–3 percentage points higher on average than the other five combinations in F1 value.


## 6. Conclusions

Innovation and entrepreneurship occupy an important position in colleges and universities. Using scientific computing methods to evaluate the effect of innovation and entrepreneurship is the content of this article. The improvement of the administration component of understudies’ advancement and business schooling is a significant issue looked in the advancement of China’s advanced education, and the present circumstance looked by schools and colleges is more convoluted. It is necessary to actively play its own advantages and try to make up for its own shortcomings in the management mechanism through continuous improvement in curriculum setting, teacher team construction, and operation mechanism. Just in this manner could we at any point genuinely take care of the ongoing issues and establish a decent starting point for the development of undergrads’ creative cognizance and the constant improvement of the degree of business venture instruction. Extending the advancement and business venture instruction of understudies has turned into the center of the ongoing thorough change of advanced education. Developing top-notch abilities with inventive reasoning is the focal point of showing change work in schools and colleges later on.



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*Source: 9928706-2022-07-05.xml*