# Research on the Reform of Higher Education Training Mode under the Background of Big Data and Internet of Things

Authors: Jing Wang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the rapid development of information processing, storage, and transmission technologies, the demand for talents from all walks of life in the society has increased, which has put forward higher requirements for the diversity of Chinese higher education. In China’s higher education, undergraduate education is in a fundamental position. China’s undergraduate education has undergone several reforms in talent training, but there are still many problems in the innovative talent training model. With the gradual improvement of the Internet of Things infrastructure, various applications characterized by big data are deeply embedded in people’s daily life. In the era of big data, the amount of data is larger and the structure is more complex. It penetrates into all fields of human life and shows unprecedented vitality and vigor. Especially in the field of higher education, the era of big data provides more information resources for education and learning and also brings new opportunities for the cultivation of applied talents in local undergraduate colleges and universities. This paper deeply studies the training mode of applied talents in local undergraduate colleges and universities under the background of big data and the Internet of Things. Based on the data analysis results of big data application technicians and industry talents, professional ability-course structure data, this paper completes the design of the corresponding talent training mode analysis system. Under the application of the system designed in this paper, it is possible to realize the analysis of the differences in the knowledge structure requirements of talents in various positions and the needs of talents’ ability, as well as the optimization and reform of the talent training curriculum system. The design of this paper can play an important guiding role in talent training in the environment of big data and Internet of Things.


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## Body

## 1. Introduction

### 1.1. Research Background

At present, the large-scale popularization and application of information technologies such as the Internet of Things, cloud computing, software-defined networks, and artificial intelligence have brought us into an era of big data with information explosion [1–4]. The era of big data not only has diverse and massive data, but also more importantly, the accompanying big data technology has a huge impact on social economy, politics, education, and other fields. In this information society where a large amount of new data may be generated every moment, the efficient and accurate data mining technology, analysis technology, and prediction function of big data will discover more data and the hidden value behind the data. Therefore, it will promote deeper changes in all areas of society [5].With the rapid development of information technology, the impact of information technology on educational reform is getting deeper and deeper, and promoting the modernization of education with information technology has become China’s national strategy [6]. In the past ten years, Chinese universities have been actively promoting the construction of informatized campuses, and the development of basic education information systems and auxiliary educational resources within campuses has also begun to take shape. With the advent of the era of big data, the degree of integration of information technology and higher education will be further deepened. New learning methods and teaching methods such as MOOCs and flipped classrooms are also emerging one after another. Countries around the world, led by the United States and Canada, have begun to actively explore the application of big data and related technologies in colleges and universities [7, 8]. China also encourages learning to use big data technology to collect, analyze, and feedback data on education and teaching activities and student behavior, to support the promotion of personalized learning and targeted teaching.The social and economic development of my country (for the convenience of readers’ understanding, if it is specified, my country in the following text refers specifically to China) is currently in an important period of industrial restructuring and upgrading, and a large number of high-quality applied talents are urgently needed. However, local undergraduate colleges and universities, as the main force in cultivating applied talents in China, have problems such as homogenization of professional settings, low quality of practical teaching, and imperfect evaluation system in the cultivation of applied talents. On the one hand, these problems have hindered the improvement of the quality of local undergraduate colleges and universities, and on the other hand, they have also led to a series of problems such as difficulties in student employment and the inability to meet the needs of social and economic development. Therefore, combined with the background of the current big data era, local undergraduate colleges and universities must assess the situation, establish data awareness, attach importance to the value of data, and strive to build college big data platforms. Today’s colleges and universities should actively understand policy changes and market demands, promote the exchange and integration of information, and use modern information technology to innovate teaching methods to improve the quality of talent training.


### 1.2. Research Motivation

The purpose of this paper is to analyze the main problems and causes of the current applied talent training model in local undergraduate colleges and universities in China. At the same time, it combines big data with the training of applied talents in local undergraduate colleges and universities to establish an effective training model for applied talents. In the big data environment, more and more data appear. Under this circumstance, deep mining, sorting, and utilization of massive data is helpful to analyze the ability needs of different occupational talents. Based on this, this paper conducts data structure and analysis for the realization of talent cultivation, in order to promote the good combination of talent cultivation and the actual needs of enterprises and provide important data support for talent cultivation.


### 1.3. Research Significance

As a new thing spawned by the development of modern information technology, big data has been studied by scholars in many fields in recent years, resulting in many academic achievements. However, there are few studies on the talent cultivation model of higher education. The main body of this study is the application-oriented talent training model in local undergraduate colleges and universities. First, the connotation of big data and the promotion effect of big data on talent training in colleges and universities are summarized in theory. Secondly, it analyzes the connotation, characteristics, main problems, and reasons of talent training mode in local undergraduate colleges and universities in my country. Finally, combining big data technology with the training mode of applied talents in local undergraduate colleges and universities, a training mode of applied talents in local undergraduate colleges and universities suitable for the era of big data is constructed. The research in this paper enriches and improves the educational theoretical system in the field of big data and the cultivation of applied talents in local undergraduate colleges and universities and provides a theoretical basis for future multiangle and in-depth research.


## 1.1. Research Background

At present, the large-scale popularization and application of information technologies such as the Internet of Things, cloud computing, software-defined networks, and artificial intelligence have brought us into an era of big data with information explosion [1–4]. The era of big data not only has diverse and massive data, but also more importantly, the accompanying big data technology has a huge impact on social economy, politics, education, and other fields. In this information society where a large amount of new data may be generated every moment, the efficient and accurate data mining technology, analysis technology, and prediction function of big data will discover more data and the hidden value behind the data. Therefore, it will promote deeper changes in all areas of society [5].With the rapid development of information technology, the impact of information technology on educational reform is getting deeper and deeper, and promoting the modernization of education with information technology has become China’s national strategy [6]. In the past ten years, Chinese universities have been actively promoting the construction of informatized campuses, and the development of basic education information systems and auxiliary educational resources within campuses has also begun to take shape. With the advent of the era of big data, the degree of integration of information technology and higher education will be further deepened. New learning methods and teaching methods such as MOOCs and flipped classrooms are also emerging one after another. Countries around the world, led by the United States and Canada, have begun to actively explore the application of big data and related technologies in colleges and universities [7, 8]. China also encourages learning to use big data technology to collect, analyze, and feedback data on education and teaching activities and student behavior, to support the promotion of personalized learning and targeted teaching.The social and economic development of my country (for the convenience of readers’ understanding, if it is specified, my country in the following text refers specifically to China) is currently in an important period of industrial restructuring and upgrading, and a large number of high-quality applied talents are urgently needed. However, local undergraduate colleges and universities, as the main force in cultivating applied talents in China, have problems such as homogenization of professional settings, low quality of practical teaching, and imperfect evaluation system in the cultivation of applied talents. On the one hand, these problems have hindered the improvement of the quality of local undergraduate colleges and universities, and on the other hand, they have also led to a series of problems such as difficulties in student employment and the inability to meet the needs of social and economic development. Therefore, combined with the background of the current big data era, local undergraduate colleges and universities must assess the situation, establish data awareness, attach importance to the value of data, and strive to build college big data platforms. Today’s colleges and universities should actively understand policy changes and market demands, promote the exchange and integration of information, and use modern information technology to innovate teaching methods to improve the quality of talent training.


## 1.2. Research Motivation

The purpose of this paper is to analyze the main problems and causes of the current applied talent training model in local undergraduate colleges and universities in China. At the same time, it combines big data with the training of applied talents in local undergraduate colleges and universities to establish an effective training model for applied talents. In the big data environment, more and more data appear. Under this circumstance, deep mining, sorting, and utilization of massive data is helpful to analyze the ability needs of different occupational talents. Based on this, this paper conducts data structure and analysis for the realization of talent cultivation, in order to promote the good combination of talent cultivation and the actual needs of enterprises and provide important data support for talent cultivation.


## 1.3. Research Significance

As a new thing spawned by the development of modern information technology, big data has been studied by scholars in many fields in recent years, resulting in many academic achievements. However, there are few studies on the talent cultivation model of higher education. The main body of this study is the application-oriented talent training model in local undergraduate colleges and universities. First, the connotation of big data and the promotion effect of big data on talent training in colleges and universities are summarized in theory. Secondly, it analyzes the connotation, characteristics, main problems, and reasons of talent training mode in local undergraduate colleges and universities in my country. Finally, combining big data technology with the training mode of applied talents in local undergraduate colleges and universities, a training mode of applied talents in local undergraduate colleges and universities suitable for the era of big data is constructed. The research in this paper enriches and improves the educational theoretical system in the field of big data and the cultivation of applied talents in local undergraduate colleges and universities and provides a theoretical basis for future multiangle and in-depth research.


## 2. Related Work

### 2.1. The Training Mode of Applied Talents in Local Undergraduate Colleges and Universities

Local undergraduate colleges and universities are a concept proposed in my country based on the classification of colleges and universities based on administrative areas. However, there are also applied universities abroad that are aimed at cultivating applied talents needed for social and economic development, such as community colleges in the United States, universities of applied sciences in Germany, and polytechnic colleges in Finland. Moreover, the cultivation of undergraduate applied talents abroad is almost half a century earlier than that in China. As early as the 1960s, western developed countries led by Germany and the United States began to pay attention to the cultivation of applied talents and carried out a large number of theoretical research and practical exploration of talent cultivation. All countries have basically formed a complete theoretical system for the cultivation of applied talents, which has cultivated a large number of high-quality applied talents for the rapid development of society and economy. At the same time, various countries have successively established application-oriented talent training models that are compatible with their own social and economic development and that are more mature in application and have their own characteristics, such as Canada’s CBE model, Germany’s dual system, and the British sandwich model. Countries such as Europe and the United States attach great importance to the cultivation of applied talents. In addition to the support and guarantee of policies and regulations, the websites of the Ministry of Education of various countries also contain a large number of relevant materials on the cultivation of applied talents. Foreign papers devoted to the cultivation of applied talents mainly focus on introducing the practice of various applied colleges and universities in the development of new courses and the construction of teaching staff. Vidal [9] introduced a course in the process of cultivating applied talents at the Technical University of Denmark. The course promotes a dramatic increase in students’ creativity and practical problem solving skills by providing them with a space to discuss, think, and experiment. Literature [10] introduced that DuPage College in the United States established an auxiliary teacher office to provide part-time teachers with whole-process services from recruitment, training to evaluation and development, helping part-time teachers to better teach and improve the quality of applied talent training. In recent years, with the increasing demand for high-quality applied talents in my country and the support of relevant policies, the research on the training mode of applied talents in local undergraduate colleges and universities has continued to heat up. The current research on the training mode of applied talents in local undergraduate colleges mainly focuses on the following three aspects. First, the basic theoretical research on the cultivation of applied talents in local undergraduate colleges and universities. Many researchers have studied from the concept and connotation of applied talents and the necessity of developing applied talents training mode in local undergraduate universities [11, 12]. As Pan and Shi [11] proposed, applied talents are generally engaged in nontheoretical research work. Its main task is to convert the abstract theoretical knowledge in books into specific operational ideas or product configurations under the guidance of certain theoretical norms and dissolve theoretical knowledge into practical work. Xu et al. pointed out that the urgent needs of social and economic development for applied talents, the responsibility of local undergraduate colleges and universities for regional development, and the development needs of local colleges and universities determine that local undergraduate colleges and universities must develop applied talents training. The second is to study the problems and countermeasures of the applied talent training mode in local undergraduate colleges and universities [13, 14]. Feng [13] pointed out that in the process of cultivating applied talents in local undergraduate colleges, there are problems such as insufficient understanding of the connotation of applied talents, insufficient teaching staff construction, and imperfect talent training system, which seriously affect the improvement of the quality of applied talents training. Local undergraduate colleges and universities must clarify the connotation of applied talents, actively build applied teachers, and deepen the reform of applied talents training mode. Literature [14] pointed out that there are serious deficiencies in the actual education and teaching of personnel training in local undergraduate colleges and universities, which is manifested in the neglect of the combination of theoretical knowledge and practical ability, resulting in the quality of application-oriented personnel training not in line with expectations. Local colleges and universities should promote the improvement of the training quality of applied talents by building production-university-research platforms and improving the practical curriculum system. The third is a case study on the training mode of applied talents in local undergraduate colleges [15]. Starting from a certain school, to carry out in-depth analysis and research on the cultivation of applied talents in local undergraduate colleges and universities is also a hot spot in the current research field of applied talents cultivation mode in local undergraduate colleges. Taking Chongqing University of Science and Technology as an example, Li [15] analyzed the main characteristics and problems of the current application-oriented talents training in local undergraduate colleges and universities. In this paper, it is suggested to reform the training mode of applied talents from the ways of coconstructing the incentive and restraint mechanism for talents training, building a diversified curriculum library, and professional industrialization, so as to promote the improvement of the training quality of applied talents.


### 2.2. Research on the Application of Big Data in Colleges and Universities

At present, big data has penetrated into various fields of national, regional and urban economic, social and public management, and the field of higher education is no exception. As an important place for cultivating high-quality talents, colleges and universities must face up to the opportunities brought by big data for the reform of daily education and teaching work. Colleges and universities should actively use big data to serve personnel training. This section summarizes the research on the application of big data in colleges and universities from the following three aspects.


#### 2.2.1. The Influence of Big Data on the Informatization Construction of Colleges and Universities

With the rapid development of information technology and the development needs of national higher education, it has become an inevitable requirement for the development of higher education in China to actively recommend the construction of education informatization. As an important force to promote the development of information technology, big data has a direct and powerful impact on the informatization construction of colleges and universities. Xu [16] believes that there are problems such as irregular data management, difficulty in data processing, and data utilization in the process of processing campus data in colleges and universities. This literature believes that the establishment of a data integration and sharing platform in colleges and universities with the help of big data technology can effectively solve these problems and promote the improvement of the level of informatization in colleges and universities.


#### 2.2.2. The Impact of Big Data on Specific Majors in Colleges and Universities

The arrival of the era of big data has brought new opportunities and challenges to all walks of life, and the same is true for various professional education in the field of higher education. Many scholars have begun to study the impact of big data on the cultivation of specific professional talents in colleges and universities. Yan [17] proposed that the arrival of the era of big data will promote the reform and innovation of the training mode of information management and information system professionals in colleges and universities. In order to cultivate information talents in the era of big data, colleges and universities should actively improve the existing talent training methods, reposition the training objectives of information management professionals according to market demand, increase the practical ability training of information management professionals, and strengthen the construction of teaching staff.


#### 2.2.3. The Impact of Big Data on Talent Training in Colleges and Universities

At present, higher education in many countries has completed quantitative expansion, and improving the quality of education has become the main task at present. The key to improving the quality of education lies in the improvement of the quality of personnel training. The arrival of the era of big data has injected new vitality into the cultivation of talents in colleges and universities. Shi [18] combined big data with talent training in local colleges and universities and proposed that in the era of big data, local colleges and universities should cultivate students’ personalized learning and innovate applied talent training methods to improve the quality of talent training.To sum up, after decades of research, my country has achieved a lot of research results in theoretical and practical research on the cultivation of applied talents in local undergraduate colleges and universities. In terms of research content, it is involved from theoretical research on the training mode of applied talents in local undergraduate colleges to problem research, and then to case studies combined with specific colleges and universities. However, there are few studies on the combination of big data and applied talent training mode in local undergraduate colleges. Among the only studies, there are few studies on the impact, role, and specific combination of big data on the training mode of applied talents in local undergraduate colleges and universities. At the same time, there are also theoretical research and background functions that only pay attention to big data and seldom explain the specific strategies of how local undergraduate colleges use big data to optimize the cultivation of applied talents, which provides direction and space for the research of this paper.


## 2.1. The Training Mode of Applied Talents in Local Undergraduate Colleges and Universities

Local undergraduate colleges and universities are a concept proposed in my country based on the classification of colleges and universities based on administrative areas. However, there are also applied universities abroad that are aimed at cultivating applied talents needed for social and economic development, such as community colleges in the United States, universities of applied sciences in Germany, and polytechnic colleges in Finland. Moreover, the cultivation of undergraduate applied talents abroad is almost half a century earlier than that in China. As early as the 1960s, western developed countries led by Germany and the United States began to pay attention to the cultivation of applied talents and carried out a large number of theoretical research and practical exploration of talent cultivation. All countries have basically formed a complete theoretical system for the cultivation of applied talents, which has cultivated a large number of high-quality applied talents for the rapid development of society and economy. At the same time, various countries have successively established application-oriented talent training models that are compatible with their own social and economic development and that are more mature in application and have their own characteristics, such as Canada’s CBE model, Germany’s dual system, and the British sandwich model. Countries such as Europe and the United States attach great importance to the cultivation of applied talents. In addition to the support and guarantee of policies and regulations, the websites of the Ministry of Education of various countries also contain a large number of relevant materials on the cultivation of applied talents. Foreign papers devoted to the cultivation of applied talents mainly focus on introducing the practice of various applied colleges and universities in the development of new courses and the construction of teaching staff. Vidal [9] introduced a course in the process of cultivating applied talents at the Technical University of Denmark. The course promotes a dramatic increase in students’ creativity and practical problem solving skills by providing them with a space to discuss, think, and experiment. Literature [10] introduced that DuPage College in the United States established an auxiliary teacher office to provide part-time teachers with whole-process services from recruitment, training to evaluation and development, helping part-time teachers to better teach and improve the quality of applied talent training. In recent years, with the increasing demand for high-quality applied talents in my country and the support of relevant policies, the research on the training mode of applied talents in local undergraduate colleges and universities has continued to heat up. The current research on the training mode of applied talents in local undergraduate colleges mainly focuses on the following three aspects. First, the basic theoretical research on the cultivation of applied talents in local undergraduate colleges and universities. Many researchers have studied from the concept and connotation of applied talents and the necessity of developing applied talents training mode in local undergraduate universities [11, 12]. As Pan and Shi [11] proposed, applied talents are generally engaged in nontheoretical research work. Its main task is to convert the abstract theoretical knowledge in books into specific operational ideas or product configurations under the guidance of certain theoretical norms and dissolve theoretical knowledge into practical work. Xu et al. pointed out that the urgent needs of social and economic development for applied talents, the responsibility of local undergraduate colleges and universities for regional development, and the development needs of local colleges and universities determine that local undergraduate colleges and universities must develop applied talents training. The second is to study the problems and countermeasures of the applied talent training mode in local undergraduate colleges and universities [13, 14]. Feng [13] pointed out that in the process of cultivating applied talents in local undergraduate colleges, there are problems such as insufficient understanding of the connotation of applied talents, insufficient teaching staff construction, and imperfect talent training system, which seriously affect the improvement of the quality of applied talents training. Local undergraduate colleges and universities must clarify the connotation of applied talents, actively build applied teachers, and deepen the reform of applied talents training mode. Literature [14] pointed out that there are serious deficiencies in the actual education and teaching of personnel training in local undergraduate colleges and universities, which is manifested in the neglect of the combination of theoretical knowledge and practical ability, resulting in the quality of application-oriented personnel training not in line with expectations. Local colleges and universities should promote the improvement of the training quality of applied talents by building production-university-research platforms and improving the practical curriculum system. The third is a case study on the training mode of applied talents in local undergraduate colleges [15]. Starting from a certain school, to carry out in-depth analysis and research on the cultivation of applied talents in local undergraduate colleges and universities is also a hot spot in the current research field of applied talents cultivation mode in local undergraduate colleges. Taking Chongqing University of Science and Technology as an example, Li [15] analyzed the main characteristics and problems of the current application-oriented talents training in local undergraduate colleges and universities. In this paper, it is suggested to reform the training mode of applied talents from the ways of coconstructing the incentive and restraint mechanism for talents training, building a diversified curriculum library, and professional industrialization, so as to promote the improvement of the training quality of applied talents.


## 2.2. Research on the Application of Big Data in Colleges and Universities

At present, big data has penetrated into various fields of national, regional and urban economic, social and public management, and the field of higher education is no exception. As an important place for cultivating high-quality talents, colleges and universities must face up to the opportunities brought by big data for the reform of daily education and teaching work. Colleges and universities should actively use big data to serve personnel training. This section summarizes the research on the application of big data in colleges and universities from the following three aspects.


### 2.2.1. The Influence of Big Data on the Informatization Construction of Colleges and Universities

With the rapid development of information technology and the development needs of national higher education, it has become an inevitable requirement for the development of higher education in China to actively recommend the construction of education informatization. As an important force to promote the development of information technology, big data has a direct and powerful impact on the informatization construction of colleges and universities. Xu [16] believes that there are problems such as irregular data management, difficulty in data processing, and data utilization in the process of processing campus data in colleges and universities. This literature believes that the establishment of a data integration and sharing platform in colleges and universities with the help of big data technology can effectively solve these problems and promote the improvement of the level of informatization in colleges and universities.


### 2.2.2. The Impact of Big Data on Specific Majors in Colleges and Universities

The arrival of the era of big data has brought new opportunities and challenges to all walks of life, and the same is true for various professional education in the field of higher education. Many scholars have begun to study the impact of big data on the cultivation of specific professional talents in colleges and universities. Yan [17] proposed that the arrival of the era of big data will promote the reform and innovation of the training mode of information management and information system professionals in colleges and universities. In order to cultivate information talents in the era of big data, colleges and universities should actively improve the existing talent training methods, reposition the training objectives of information management professionals according to market demand, increase the practical ability training of information management professionals, and strengthen the construction of teaching staff.


### 2.2.3. The Impact of Big Data on Talent Training in Colleges and Universities

At present, higher education in many countries has completed quantitative expansion, and improving the quality of education has become the main task at present. The key to improving the quality of education lies in the improvement of the quality of personnel training. The arrival of the era of big data has injected new vitality into the cultivation of talents in colleges and universities. Shi [18] combined big data with talent training in local colleges and universities and proposed that in the era of big data, local colleges and universities should cultivate students’ personalized learning and innovate applied talent training methods to improve the quality of talent training.To sum up, after decades of research, my country has achieved a lot of research results in theoretical and practical research on the cultivation of applied talents in local undergraduate colleges and universities. In terms of research content, it is involved from theoretical research on the training mode of applied talents in local undergraduate colleges to problem research, and then to case studies combined with specific colleges and universities. However, there are few studies on the combination of big data and applied talent training mode in local undergraduate colleges. Among the only studies, there are few studies on the impact, role, and specific combination of big data on the training mode of applied talents in local undergraduate colleges and universities. At the same time, there are also theoretical research and background functions that only pay attention to big data and seldom explain the specific strategies of how local undergraduate colleges use big data to optimize the cultivation of applied talents, which provides direction and space for the research of this paper.


## 2.2.1. The Influence of Big Data on the Informatization Construction of Colleges and Universities

With the rapid development of information technology and the development needs of national higher education, it has become an inevitable requirement for the development of higher education in China to actively recommend the construction of education informatization. As an important force to promote the development of information technology, big data has a direct and powerful impact on the informatization construction of colleges and universities. Xu [16] believes that there are problems such as irregular data management, difficulty in data processing, and data utilization in the process of processing campus data in colleges and universities. This literature believes that the establishment of a data integration and sharing platform in colleges and universities with the help of big data technology can effectively solve these problems and promote the improvement of the level of informatization in colleges and universities.


## 2.2.2. The Impact of Big Data on Specific Majors in Colleges and Universities

The arrival of the era of big data has brought new opportunities and challenges to all walks of life, and the same is true for various professional education in the field of higher education. Many scholars have begun to study the impact of big data on the cultivation of specific professional talents in colleges and universities. Yan [17] proposed that the arrival of the era of big data will promote the reform and innovation of the training mode of information management and information system professionals in colleges and universities. In order to cultivate information talents in the era of big data, colleges and universities should actively improve the existing talent training methods, reposition the training objectives of information management professionals according to market demand, increase the practical ability training of information management professionals, and strengthen the construction of teaching staff.


## 2.2.3. The Impact of Big Data on Talent Training in Colleges and Universities

At present, higher education in many countries has completed quantitative expansion, and improving the quality of education has become the main task at present. The key to improving the quality of education lies in the improvement of the quality of personnel training. The arrival of the era of big data has injected new vitality into the cultivation of talents in colleges and universities. Shi [18] combined big data with talent training in local colleges and universities and proposed that in the era of big data, local colleges and universities should cultivate students’ personalized learning and innovate applied talent training methods to improve the quality of talent training.To sum up, after decades of research, my country has achieved a lot of research results in theoretical and practical research on the cultivation of applied talents in local undergraduate colleges and universities. In terms of research content, it is involved from theoretical research on the training mode of applied talents in local undergraduate colleges to problem research, and then to case studies combined with specific colleges and universities. However, there are few studies on the combination of big data and applied talent training mode in local undergraduate colleges. Among the only studies, there are few studies on the impact, role, and specific combination of big data on the training mode of applied talents in local undergraduate colleges and universities. At the same time, there are also theoretical research and background functions that only pay attention to big data and seldom explain the specific strategies of how local undergraduate colleges use big data to optimize the cultivation of applied talents, which provides direction and space for the research of this paper.


## 3. The Application of Big Data in the Cultivation of Talents in Colleges and Universities

### 3.1. The Connotation of Big Data and the Internet of Things

Big data refers to datasets whose size exceeds the processing capacity of conventional database tools. Its significance is to help analyze and study various types of datasets, extract valuable information, and help people make scientific decisions when solving problems. Big data presents different characteristics from traditional data, which can be summarized as 4 Vs. First, the massive data capacity (volume). The data volume of big data is huge, and the data scale is more complete than ever. In the past, the data that needed to be processed was generally in the gigabyte to terabyte level. However, due to the improvement of data mining processing technology, the rapid development of network bandwidth, and the widespread use of mobile smart terminals and social networks, the amount of data generated and stored in both virtual networks and the real world is increasing explosively. The data scale in the era of big data has far exceeded the largest level in the past, rising to the petabyte level, and may even exceed the ZB level in the future. Second, the variety of data types (variety). There are many types of data in the era of big data. Most of the original data can be stored in the database in the form of two-dimensional tables. However, with the large-scale popularization and application of the Internet of Things, social networks, and various types of mobile intelligent terminals, the rich and colorful network life has also led to the continuous increase of data of various types of structures. There are more and more unstructured data such as videos, pictures, blogs, and geographic locations, the data structure is more and more complex, and it also increases the difficulty of data processing. Third, high-speed processing (velocity). In addition to the massive data capacity and diverse data types, big data also requires high-speed real-time processing of data. Due to the infinite expansion of data scale, the high-speed processing of big data requires both the shortness of time and the decision-making ability of processing, and it is required to capture the key information of important events in the shortest time. This is not only the difficulty of big data mining processing technology but also the characteristic of modern big data technology that is different from traditional data technology. Fourth, low-density value (value). Although the scale of big data is huge, these data show more uncertainty, instability, and scarcity. People often need to pan for gold from massive amounts of data, looking for valuable small amounts of data. For example, in a video that is several hours long, maybe only a second or two of data video is valuable to us.The knowledge related to big data covers all aspects of the data from the original state to the abstract application. The distribution of the main content is shown in Figure1. In Figure 1, the bottom layer is the primitive type of data. The next layer is data organization and management tools, such as traditional structured databases such as Oracle and SQL Server. There are also unstructured databases that have emerged in recent years, such as Flume and Sqoop. The third layer from the bottom up is mainly the data mining methods and software tools designed for mathematical statistics, query, analysis, and reporting of data. The top layer is the display and application of data, such as data visualization. The top-level application can display the final analysis results to users through visual means, helping users make more efficient decisions.Figure 1
Big data knowledge connotation.As we all know, the Internet of Things was first proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology in the study of radiofrequency identification technology. The Internet of Things initially refers to the combination of radiofrequency identification technology and equipment, and the combination of the agreed communication protocol and the Internet to achieve automatic collection and intelligent management of item information. At present, the connotation of the Internet of Things has been greatly enriched. It is a network that can connect various items to the Internet, thereby realizing data exchange and communication. The Internet of Things can intelligently identify, locate, track, monitor, and manage networked items. The core idea of IoT is shown in Figure2. From the connotation of the Internet of Things, it is not difficult to find that data collection and data transmission, which are not covered by big data-related majors, can be included in the scope of the Internet of Things. This further shows that the development of big data-related majors will not conflict with the existing IoT engineering majors. In fact, the construction of the Internet of Things engineering can deliver talents for the industrial application of big data. This means that talent training in IoT engineering still plays an important role in the context of big data.Figure 2
Concept illustration of Internet of Things.


### 3.2. The Role of Big Data and Its Application in Promoting Talent Training in Colleges and Universities

#### 3.2.1. The Creation of New Educational Methods and Teaching Methods

At present, with the support of massive educational resources and big data-related technologies developed on the Internet, various new educational methods and teaching methods are constantly emerging. Students are no longer limited to acquiring knowledge in the classroom, and teachers are no longer limited to traditional teaching methods. The teaching process and learning process will break through the limitations of school teaching time and space. In terms of classroom teaching organization, it has gradually developed from traditional closed classroom teaching to semiopen blended courses and finally to fully open socialized course teaching. The new teaching methods developed with the help of information technology will also be a useful supplement to the traditional teaching methods. For example, with the help of learning analysis technology based on big data technology, MOOC provides more effective humanized learning support for students’ learning through the evaluation of students’ knowledge construction and complex ability.


#### 3.2.2. Improving the Quality of Student Employment

The success of employment is one of the important criteria that directly reflects the quality of talent training in colleges and universities. Colleges and universities can use big data technology to recommend suitable careers and positions for students by collecting relevant information such as students’ grades, interests, hobbies, skills, and job-related information. In this way, it can not only improve the employment rate of college students but also improve the employment satisfaction of both employers and students and achieve high-quality employment for college graduates. At the same time, after students are successfully employed, colleges and universities should also actively use big data technology, establish a big data platform for student employment services, and conduct follow-up visits to students by means of emails and telephone interviews. The school can provide students with industry-related policy consultation and psychological counseling, so that students can successfully complete the transition from school to workplace and improve the entire process of college talent training.


## 3.1. The Connotation of Big Data and the Internet of Things

Big data refers to datasets whose size exceeds the processing capacity of conventional database tools. Its significance is to help analyze and study various types of datasets, extract valuable information, and help people make scientific decisions when solving problems. Big data presents different characteristics from traditional data, which can be summarized as 4 Vs. First, the massive data capacity (volume). The data volume of big data is huge, and the data scale is more complete than ever. In the past, the data that needed to be processed was generally in the gigabyte to terabyte level. However, due to the improvement of data mining processing technology, the rapid development of network bandwidth, and the widespread use of mobile smart terminals and social networks, the amount of data generated and stored in both virtual networks and the real world is increasing explosively. The data scale in the era of big data has far exceeded the largest level in the past, rising to the petabyte level, and may even exceed the ZB level in the future. Second, the variety of data types (variety). There are many types of data in the era of big data. Most of the original data can be stored in the database in the form of two-dimensional tables. However, with the large-scale popularization and application of the Internet of Things, social networks, and various types of mobile intelligent terminals, the rich and colorful network life has also led to the continuous increase of data of various types of structures. There are more and more unstructured data such as videos, pictures, blogs, and geographic locations, the data structure is more and more complex, and it also increases the difficulty of data processing. Third, high-speed processing (velocity). In addition to the massive data capacity and diverse data types, big data also requires high-speed real-time processing of data. Due to the infinite expansion of data scale, the high-speed processing of big data requires both the shortness of time and the decision-making ability of processing, and it is required to capture the key information of important events in the shortest time. This is not only the difficulty of big data mining processing technology but also the characteristic of modern big data technology that is different from traditional data technology. Fourth, low-density value (value). Although the scale of big data is huge, these data show more uncertainty, instability, and scarcity. People often need to pan for gold from massive amounts of data, looking for valuable small amounts of data. For example, in a video that is several hours long, maybe only a second or two of data video is valuable to us.The knowledge related to big data covers all aspects of the data from the original state to the abstract application. The distribution of the main content is shown in Figure1. In Figure 1, the bottom layer is the primitive type of data. The next layer is data organization and management tools, such as traditional structured databases such as Oracle and SQL Server. There are also unstructured databases that have emerged in recent years, such as Flume and Sqoop. The third layer from the bottom up is mainly the data mining methods and software tools designed for mathematical statistics, query, analysis, and reporting of data. The top layer is the display and application of data, such as data visualization. The top-level application can display the final analysis results to users through visual means, helping users make more efficient decisions.Figure 1
Big data knowledge connotation.As we all know, the Internet of Things was first proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology in the study of radiofrequency identification technology. The Internet of Things initially refers to the combination of radiofrequency identification technology and equipment, and the combination of the agreed communication protocol and the Internet to achieve automatic collection and intelligent management of item information. At present, the connotation of the Internet of Things has been greatly enriched. It is a network that can connect various items to the Internet, thereby realizing data exchange and communication. The Internet of Things can intelligently identify, locate, track, monitor, and manage networked items. The core idea of IoT is shown in Figure2. From the connotation of the Internet of Things, it is not difficult to find that data collection and data transmission, which are not covered by big data-related majors, can be included in the scope of the Internet of Things. This further shows that the development of big data-related majors will not conflict with the existing IoT engineering majors. In fact, the construction of the Internet of Things engineering can deliver talents for the industrial application of big data. This means that talent training in IoT engineering still plays an important role in the context of big data.Figure 2
Concept illustration of Internet of Things.


## 3.2. The Role of Big Data and Its Application in Promoting Talent Training in Colleges and Universities

### 3.2.1. The Creation of New Educational Methods and Teaching Methods

At present, with the support of massive educational resources and big data-related technologies developed on the Internet, various new educational methods and teaching methods are constantly emerging. Students are no longer limited to acquiring knowledge in the classroom, and teachers are no longer limited to traditional teaching methods. The teaching process and learning process will break through the limitations of school teaching time and space. In terms of classroom teaching organization, it has gradually developed from traditional closed classroom teaching to semiopen blended courses and finally to fully open socialized course teaching. The new teaching methods developed with the help of information technology will also be a useful supplement to the traditional teaching methods. For example, with the help of learning analysis technology based on big data technology, MOOC provides more effective humanized learning support for students’ learning through the evaluation of students’ knowledge construction and complex ability.


### 3.2.2. Improving the Quality of Student Employment

The success of employment is one of the important criteria that directly reflects the quality of talent training in colleges and universities. Colleges and universities can use big data technology to recommend suitable careers and positions for students by collecting relevant information such as students’ grades, interests, hobbies, skills, and job-related information. In this way, it can not only improve the employment rate of college students but also improve the employment satisfaction of both employers and students and achieve high-quality employment for college graduates. At the same time, after students are successfully employed, colleges and universities should also actively use big data technology, establish a big data platform for student employment services, and conduct follow-up visits to students by means of emails and telephone interviews. The school can provide students with industry-related policy consultation and psychological counseling, so that students can successfully complete the transition from school to workplace and improve the entire process of college talent training.


## 3.2.1. The Creation of New Educational Methods and Teaching Methods

At present, with the support of massive educational resources and big data-related technologies developed on the Internet, various new educational methods and teaching methods are constantly emerging. Students are no longer limited to acquiring knowledge in the classroom, and teachers are no longer limited to traditional teaching methods. The teaching process and learning process will break through the limitations of school teaching time and space. In terms of classroom teaching organization, it has gradually developed from traditional closed classroom teaching to semiopen blended courses and finally to fully open socialized course teaching. The new teaching methods developed with the help of information technology will also be a useful supplement to the traditional teaching methods. For example, with the help of learning analysis technology based on big data technology, MOOC provides more effective humanized learning support for students’ learning through the evaluation of students’ knowledge construction and complex ability.


## 3.2.2. Improving the Quality of Student Employment

The success of employment is one of the important criteria that directly reflects the quality of talent training in colleges and universities. Colleges and universities can use big data technology to recommend suitable careers and positions for students by collecting relevant information such as students’ grades, interests, hobbies, skills, and job-related information. In this way, it can not only improve the employment rate of college students but also improve the employment satisfaction of both employers and students and achieve high-quality employment for college graduates. At the same time, after students are successfully employed, colleges and universities should also actively use big data technology, establish a big data platform for student employment services, and conduct follow-up visits to students by means of emails and telephone interviews. The school can provide students with industry-related policy consultation and psychological counseling, so that students can successfully complete the transition from school to workplace and improve the entire process of college talent training.


## 4. Design of Talent Training Mode Analysis System Based on Big Data Environment

### 4.1. Function and Application of Talent Training Mode Analysis System

The main functions of the talent training model analysis system are as follows.


#### 4.1.1. Statistics Summary Function

First of all, in the system, the relevant data of office, teaching, scientific research, and employment in the current teaching development of colleges and universities should be collected and sorted, and they should be aggregated to implement statistics. At the same time, the obtained data can be applied in various management work in colleges and universities to provide support for data query.


#### 4.1.2. Manage Monitoring Functions

Aiming at the relevant data of teachers’ performance and students’ grades and credits in the current talent training work in colleges and universities, dynamic monitoring and management are implemented, so as to grasp the actual situation of students’ learning in time. On the basis of analyzing the development trend of relevant data, it is also possible to discover the existing problems in time and then formulate corresponding control strategies. The talent training mode analysis system can formulate targeted improvement countermeasures for talent training, thereby improving the quality of talent training.


#### 4.1.3. Comparative Analysis Functions

The construction of university data centers based on the data related to the status of talent training in colleges and universities can carry out statistical comparative analysis on the talent training information of different colleges and universities, which can help colleges and universities to understand different talent training information in a timely manner and achieve continuous improvement of talent training work. Monitoring the state of running a school means that under the application of the system data summary and statistics function, the advantages and disadvantages of the university development and talent training model can be found in time, and the shortcomings can be further improved while exerting their own advantages.The use of big data technology to build a talent training model focuses on completing the construction of the knowledge chain of professional courses in talent training. The knowledge-driven process can provide the corresponding process for solving problems in the industry field. Each process needs to have certain capabilities in the application of knowledge. That is, in the process of solving problems in the industry field, it is driven by the knowledge-promoting process and then constructs the capability requirement chain and constructs the corresponding knowledge module requirements under this requirement. Its specific construction process is shown in Figure3.Figure 3
The construction process of curriculum system under talent cultivation.


### 4.2. Design of Talent Training Mode Analysis System Based on Big Data Environment

#### 4.2.1. Statistical Analysis of Industry Talent Demand Data

The demand for talents in the industry is not only the focus of enterprises but also an important reference for colleges and universities in talent training. For the field of employment, the analysis can be carried out in combination with the needs of enterprises, demand trends, popular needs, and career selection factors. The main measures are as follows: perform statistical analysis on the needs of talents and realize the overall understanding of the needs of talents. In the analysis process, the focus is to carry out analysis based on industry recruitment, such as statistical analysis of relevant information such as popular industries, occupations, and regions. For example, you can have an understanding of the important content of the industry needs and understand the actual needs of the industry.


#### 4.2.2. The Conversion Relationship between Courses and Competencies

In the training of modern talents, there are three main lines in the design of the curriculum system framework based on the application of practical ability. Through the further analysis of the process chain in the business stage, relevant experts will put forward corresponding suggestions for the talent needs of this industry, so as to realize the construction of the chain of competencies and modules. This requires not only an analysis of professional competencies in the industry but also an exploration of their relationship to the curriculum. In talent cultivation, courses are the main teaching method to impart knowledge, so the analysis of the relationship between professional ability and knowledge can be realized. Ability is the main realization purpose in knowledge learning. The ability of talents can be divided into cognitive ability and practical ability. The conversion of knowledge to cognitive ability is to realize the conversion of cognitive ability based on the psychological horizon and then convert it into practical ability and finally realize the unity of psychological process and human body activities. When cultivating the practical ability of talents, it is necessary to realize the application of individual external tools and environments. The conversion relationship between courses and abilities is shown in Figure4.Figure 4
Curriculum and ability transformation relationship psychological activities.


#### 4.2.3. Solving the Relationship between Professional Ability and Curriculum Structure Based on LDA Topic Model

The main function of the LDA model is to extract understandable and stable latent semantic structures. Each “job description” data contains different “ability” requirements in this position, and different skills usually appear at the same time as part of the vocabulary. Equating each “job description” with different articles, on this basis, different professional abilities are also a “topic,” and it is a “topic” composed of multiple topic words. A number of “topics” can be generated by performing calculations on it, from which position competencies in this industry knowledge can be derived.In the derivation of ability and curriculum structure, it can be divided into two stages, namely, “computational ability theme” and “competence-course structure transformation.” The process of the first stage is as follows: the preferred classification summarizes the corresponding “business stage” relationship of the occupation. Preprocessing is carried out for the description data in different positions, mainly for the removal of corresponding text information, and the word segmentation of numbers and punctuation marks. In addition, for the preprocessed job description results, the LDA topic model is used to implement the “ability” topic model mining. The word frequency statistics can be used for statistical analysis with the relevant texts of “tools” to determine the corresponding popular technologies and provide corresponding basis for the selection of practical teaching tools in the process of talent training. The second stage is to implement parameter calculation under the application of the LDA model for the number of topics, so as to obtain relatively independent topics with outstanding related “capabilities.” Artificial refinement is performed on the resulting topics, and the topics are classified under the criteria of cognitive ability and practical ability. According to the ability requirements of different stages, the cognitive layer and the application layer shall prevail in the process of division, and the corresponding courses can be set under the corresponding talent training goals.


#### 4.2.4. Implementation of Competency and Course Structure Analysis

According to the above classification results, for the corresponding occupations, the research objects selected in this paper are the occupations of data acquisition engineer, data algorithm engineer, and data platform/warehouse engineer. We collect, store, model, and analyze its data. In the process of job category classification, big data technology can not only provide massive data but also provide corresponding technical support for data mining analysis. The basic processing flow is shown in Figure5.Figure 5
The basic process of big data processing industry problems.The professional curriculum framework of data application technology under the application of LDA model is shown in Figure6. The “?” in the figure is the content that has not yet been determined. From the analysis of ability and course results, it can be seen that the classification and processing of massive data of positions can be realized through big data technology. Based on the LDA topic model, the processing and refining of the corresponding job topics are realized. In the career analysis selected in this article, the main topics extracted are being familiar with the network, the principles of web pages, mastering JAVA, having data capture experience, providing job benefits, being familiar with data architecture and algorithms, and being familiar with data collection protocols. In the relevant keywords, the offer of job incentives was deleted, and this topic is the content of compensation and benefits. Five topics were finalized in the competency hierarchy analysis.Figure 6
Data science and big data application technology professional curriculum framework.These topics can be divided into two types according to ability. One is cognitive ability, that is, being familiar with the principles of web pages, familiar with data structures and algorithms, and familiar with data collection protocols. The other is practical ability, including two topics of data collection language tools and data capture experience. Combined with the cognitive ability and time ability obtained from the above research, the corresponding education and teaching content can be specified, and the corresponding curriculum can be set. Based on this, it is also possible to construct a corresponding talent training model according to the corresponding job requirements and ability requirements. There are differences in the knowledge requirements for talents in different positions, and the knowledge requirements for talents in different positions also overlap. In the process of talent training, it is necessary to realize the statistical analysis of the position knowledge structure and its ability requirements under the application of big data technology, and it is necessary to realize the training of talents according to the target positioning of the school. In the training process, the training of talents in 1-2 positions can be the focus. In the course setting, it is also necessary to achieve consistency with the job category competency requirements and course content. In the process of talent training in colleges and universities, generalist training can also be implemented for teachers and students in the whole school, and on this basis, the integration of corresponding job courses can be realized.


## 4.1. Function and Application of Talent Training Mode Analysis System

The main functions of the talent training model analysis system are as follows.


### 4.1.1. Statistics Summary Function

First of all, in the system, the relevant data of office, teaching, scientific research, and employment in the current teaching development of colleges and universities should be collected and sorted, and they should be aggregated to implement statistics. At the same time, the obtained data can be applied in various management work in colleges and universities to provide support for data query.


### 4.1.2. Manage Monitoring Functions

Aiming at the relevant data of teachers’ performance and students’ grades and credits in the current talent training work in colleges and universities, dynamic monitoring and management are implemented, so as to grasp the actual situation of students’ learning in time. On the basis of analyzing the development trend of relevant data, it is also possible to discover the existing problems in time and then formulate corresponding control strategies. The talent training mode analysis system can formulate targeted improvement countermeasures for talent training, thereby improving the quality of talent training.


### 4.1.3. Comparative Analysis Functions

The construction of university data centers based on the data related to the status of talent training in colleges and universities can carry out statistical comparative analysis on the talent training information of different colleges and universities, which can help colleges and universities to understand different talent training information in a timely manner and achieve continuous improvement of talent training work. Monitoring the state of running a school means that under the application of the system data summary and statistics function, the advantages and disadvantages of the university development and talent training model can be found in time, and the shortcomings can be further improved while exerting their own advantages.The use of big data technology to build a talent training model focuses on completing the construction of the knowledge chain of professional courses in talent training. The knowledge-driven process can provide the corresponding process for solving problems in the industry field. Each process needs to have certain capabilities in the application of knowledge. That is, in the process of solving problems in the industry field, it is driven by the knowledge-promoting process and then constructs the capability requirement chain and constructs the corresponding knowledge module requirements under this requirement. Its specific construction process is shown in Figure3.Figure 3
The construction process of curriculum system under talent cultivation.


## 4.1.1. Statistics Summary Function

First of all, in the system, the relevant data of office, teaching, scientific research, and employment in the current teaching development of colleges and universities should be collected and sorted, and they should be aggregated to implement statistics. At the same time, the obtained data can be applied in various management work in colleges and universities to provide support for data query.


## 4.1.2. Manage Monitoring Functions

Aiming at the relevant data of teachers’ performance and students’ grades and credits in the current talent training work in colleges and universities, dynamic monitoring and management are implemented, so as to grasp the actual situation of students’ learning in time. On the basis of analyzing the development trend of relevant data, it is also possible to discover the existing problems in time and then formulate corresponding control strategies. The talent training mode analysis system can formulate targeted improvement countermeasures for talent training, thereby improving the quality of talent training.


## 4.1.3. Comparative Analysis Functions

The construction of university data centers based on the data related to the status of talent training in colleges and universities can carry out statistical comparative analysis on the talent training information of different colleges and universities, which can help colleges and universities to understand different talent training information in a timely manner and achieve continuous improvement of talent training work. Monitoring the state of running a school means that under the application of the system data summary and statistics function, the advantages and disadvantages of the university development and talent training model can be found in time, and the shortcomings can be further improved while exerting their own advantages.The use of big data technology to build a talent training model focuses on completing the construction of the knowledge chain of professional courses in talent training. The knowledge-driven process can provide the corresponding process for solving problems in the industry field. Each process needs to have certain capabilities in the application of knowledge. That is, in the process of solving problems in the industry field, it is driven by the knowledge-promoting process and then constructs the capability requirement chain and constructs the corresponding knowledge module requirements under this requirement. Its specific construction process is shown in Figure3.Figure 3
The construction process of curriculum system under talent cultivation.


## 4.2. Design of Talent Training Mode Analysis System Based on Big Data Environment

### 4.2.1. Statistical Analysis of Industry Talent Demand Data

The demand for talents in the industry is not only the focus of enterprises but also an important reference for colleges and universities in talent training. For the field of employment, the analysis can be carried out in combination with the needs of enterprises, demand trends, popular needs, and career selection factors. The main measures are as follows: perform statistical analysis on the needs of talents and realize the overall understanding of the needs of talents. In the analysis process, the focus is to carry out analysis based on industry recruitment, such as statistical analysis of relevant information such as popular industries, occupations, and regions. For example, you can have an understanding of the important content of the industry needs and understand the actual needs of the industry.


### 4.2.2. The Conversion Relationship between Courses and Competencies

In the training of modern talents, there are three main lines in the design of the curriculum system framework based on the application of practical ability. Through the further analysis of the process chain in the business stage, relevant experts will put forward corresponding suggestions for the talent needs of this industry, so as to realize the construction of the chain of competencies and modules. This requires not only an analysis of professional competencies in the industry but also an exploration of their relationship to the curriculum. In talent cultivation, courses are the main teaching method to impart knowledge, so the analysis of the relationship between professional ability and knowledge can be realized. Ability is the main realization purpose in knowledge learning. The ability of talents can be divided into cognitive ability and practical ability. The conversion of knowledge to cognitive ability is to realize the conversion of cognitive ability based on the psychological horizon and then convert it into practical ability and finally realize the unity of psychological process and human body activities. When cultivating the practical ability of talents, it is necessary to realize the application of individual external tools and environments. The conversion relationship between courses and abilities is shown in Figure4.Figure 4
Curriculum and ability transformation relationship psychological activities.


### 4.2.3. Solving the Relationship between Professional Ability and Curriculum Structure Based on LDA Topic Model

The main function of the LDA model is to extract understandable and stable latent semantic structures. Each “job description” data contains different “ability” requirements in this position, and different skills usually appear at the same time as part of the vocabulary. Equating each “job description” with different articles, on this basis, different professional abilities are also a “topic,” and it is a “topic” composed of multiple topic words. A number of “topics” can be generated by performing calculations on it, from which position competencies in this industry knowledge can be derived.In the derivation of ability and curriculum structure, it can be divided into two stages, namely, “computational ability theme” and “competence-course structure transformation.” The process of the first stage is as follows: the preferred classification summarizes the corresponding “business stage” relationship of the occupation. Preprocessing is carried out for the description data in different positions, mainly for the removal of corresponding text information, and the word segmentation of numbers and punctuation marks. In addition, for the preprocessed job description results, the LDA topic model is used to implement the “ability” topic model mining. The word frequency statistics can be used for statistical analysis with the relevant texts of “tools” to determine the corresponding popular technologies and provide corresponding basis for the selection of practical teaching tools in the process of talent training. The second stage is to implement parameter calculation under the application of the LDA model for the number of topics, so as to obtain relatively independent topics with outstanding related “capabilities.” Artificial refinement is performed on the resulting topics, and the topics are classified under the criteria of cognitive ability and practical ability. According to the ability requirements of different stages, the cognitive layer and the application layer shall prevail in the process of division, and the corresponding courses can be set under the corresponding talent training goals.


### 4.2.4. Implementation of Competency and Course Structure Analysis

According to the above classification results, for the corresponding occupations, the research objects selected in this paper are the occupations of data acquisition engineer, data algorithm engineer, and data platform/warehouse engineer. We collect, store, model, and analyze its data. In the process of job category classification, big data technology can not only provide massive data but also provide corresponding technical support for data mining analysis. The basic processing flow is shown in Figure5.Figure 5
The basic process of big data processing industry problems.The professional curriculum framework of data application technology under the application of LDA model is shown in Figure6. The “?” in the figure is the content that has not yet been determined. From the analysis of ability and course results, it can be seen that the classification and processing of massive data of positions can be realized through big data technology. Based on the LDA topic model, the processing and refining of the corresponding job topics are realized. In the career analysis selected in this article, the main topics extracted are being familiar with the network, the principles of web pages, mastering JAVA, having data capture experience, providing job benefits, being familiar with data architecture and algorithms, and being familiar with data collection protocols. In the relevant keywords, the offer of job incentives was deleted, and this topic is the content of compensation and benefits. Five topics were finalized in the competency hierarchy analysis.Figure 6
Data science and big data application technology professional curriculum framework.These topics can be divided into two types according to ability. One is cognitive ability, that is, being familiar with the principles of web pages, familiar with data structures and algorithms, and familiar with data collection protocols. The other is practical ability, including two topics of data collection language tools and data capture experience. Combined with the cognitive ability and time ability obtained from the above research, the corresponding education and teaching content can be specified, and the corresponding curriculum can be set. Based on this, it is also possible to construct a corresponding talent training model according to the corresponding job requirements and ability requirements. There are differences in the knowledge requirements for talents in different positions, and the knowledge requirements for talents in different positions also overlap. In the process of talent training, it is necessary to realize the statistical analysis of the position knowledge structure and its ability requirements under the application of big data technology, and it is necessary to realize the training of talents according to the target positioning of the school. In the training process, the training of talents in 1-2 positions can be the focus. In the course setting, it is also necessary to achieve consistency with the job category competency requirements and course content. In the process of talent training in colleges and universities, generalist training can also be implemented for teachers and students in the whole school, and on this basis, the integration of corresponding job courses can be realized.


## 4.2.1. Statistical Analysis of Industry Talent Demand Data

The demand for talents in the industry is not only the focus of enterprises but also an important reference for colleges and universities in talent training. For the field of employment, the analysis can be carried out in combination with the needs of enterprises, demand trends, popular needs, and career selection factors. The main measures are as follows: perform statistical analysis on the needs of talents and realize the overall understanding of the needs of talents. In the analysis process, the focus is to carry out analysis based on industry recruitment, such as statistical analysis of relevant information such as popular industries, occupations, and regions. For example, you can have an understanding of the important content of the industry needs and understand the actual needs of the industry.


## 4.2.2. The Conversion Relationship between Courses and Competencies

In the training of modern talents, there are three main lines in the design of the curriculum system framework based on the application of practical ability. Through the further analysis of the process chain in the business stage, relevant experts will put forward corresponding suggestions for the talent needs of this industry, so as to realize the construction of the chain of competencies and modules. This requires not only an analysis of professional competencies in the industry but also an exploration of their relationship to the curriculum. In talent cultivation, courses are the main teaching method to impart knowledge, so the analysis of the relationship between professional ability and knowledge can be realized. Ability is the main realization purpose in knowledge learning. The ability of talents can be divided into cognitive ability and practical ability. The conversion of knowledge to cognitive ability is to realize the conversion of cognitive ability based on the psychological horizon and then convert it into practical ability and finally realize the unity of psychological process and human body activities. When cultivating the practical ability of talents, it is necessary to realize the application of individual external tools and environments. The conversion relationship between courses and abilities is shown in Figure4.Figure 4
Curriculum and ability transformation relationship psychological activities.


## 4.2.3. Solving the Relationship between Professional Ability and Curriculum Structure Based on LDA Topic Model

The main function of the LDA model is to extract understandable and stable latent semantic structures. Each “job description” data contains different “ability” requirements in this position, and different skills usually appear at the same time as part of the vocabulary. Equating each “job description” with different articles, on this basis, different professional abilities are also a “topic,” and it is a “topic” composed of multiple topic words. A number of “topics” can be generated by performing calculations on it, from which position competencies in this industry knowledge can be derived.In the derivation of ability and curriculum structure, it can be divided into two stages, namely, “computational ability theme” and “competence-course structure transformation.” The process of the first stage is as follows: the preferred classification summarizes the corresponding “business stage” relationship of the occupation. Preprocessing is carried out for the description data in different positions, mainly for the removal of corresponding text information, and the word segmentation of numbers and punctuation marks. In addition, for the preprocessed job description results, the LDA topic model is used to implement the “ability” topic model mining. The word frequency statistics can be used for statistical analysis with the relevant texts of “tools” to determine the corresponding popular technologies and provide corresponding basis for the selection of practical teaching tools in the process of talent training. The second stage is to implement parameter calculation under the application of the LDA model for the number of topics, so as to obtain relatively independent topics with outstanding related “capabilities.” Artificial refinement is performed on the resulting topics, and the topics are classified under the criteria of cognitive ability and practical ability. According to the ability requirements of different stages, the cognitive layer and the application layer shall prevail in the process of division, and the corresponding courses can be set under the corresponding talent training goals.


## 4.2.4. Implementation of Competency and Course Structure Analysis

According to the above classification results, for the corresponding occupations, the research objects selected in this paper are the occupations of data acquisition engineer, data algorithm engineer, and data platform/warehouse engineer. We collect, store, model, and analyze its data. In the process of job category classification, big data technology can not only provide massive data but also provide corresponding technical support for data mining analysis. The basic processing flow is shown in Figure5.Figure 5
The basic process of big data processing industry problems.The professional curriculum framework of data application technology under the application of LDA model is shown in Figure6. The “?” in the figure is the content that has not yet been determined. From the analysis of ability and course results, it can be seen that the classification and processing of massive data of positions can be realized through big data technology. Based on the LDA topic model, the processing and refining of the corresponding job topics are realized. In the career analysis selected in this article, the main topics extracted are being familiar with the network, the principles of web pages, mastering JAVA, having data capture experience, providing job benefits, being familiar with data architecture and algorithms, and being familiar with data collection protocols. In the relevant keywords, the offer of job incentives was deleted, and this topic is the content of compensation and benefits. Five topics were finalized in the competency hierarchy analysis.Figure 6
Data science and big data application technology professional curriculum framework.These topics can be divided into two types according to ability. One is cognitive ability, that is, being familiar with the principles of web pages, familiar with data structures and algorithms, and familiar with data collection protocols. The other is practical ability, including two topics of data collection language tools and data capture experience. Combined with the cognitive ability and time ability obtained from the above research, the corresponding education and teaching content can be specified, and the corresponding curriculum can be set. Based on this, it is also possible to construct a corresponding talent training model according to the corresponding job requirements and ability requirements. There are differences in the knowledge requirements for talents in different positions, and the knowledge requirements for talents in different positions also overlap. In the process of talent training, it is necessary to realize the statistical analysis of the position knowledge structure and its ability requirements under the application of big data technology, and it is necessary to realize the training of talents according to the target positioning of the school. In the training process, the training of talents in 1-2 positions can be the focus. In the course setting, it is also necessary to achieve consistency with the job category competency requirements and course content. In the process of talent training in colleges and universities, generalist training can also be implemented for teachers and students in the whole school, and on this basis, the integration of corresponding job courses can be realized.


## 5. Conclusion

The construction of the talent training mode analysis system in the big data environment must first understand the current talent needs, social needs, and the actual needs of corporate positions. At the same time, it is also necessary to realize the combination with the actual template positioning of colleges and universities. Through the analysis of the function and application value of the talent training mode analysis system in this paper, it shows that it can be applied in talent training. If it is necessary to improve the effectiveness of the talent training model in colleges and universities, it is necessary to realize the transformation of courses and abilities, and only based on this can the talent training model be reasonably set up. The framework design of talent ability and course structure has high industry professionalism, so it can be promoted and applied more. In the process of talent training in colleges and universities, attention should be paid to improving the feasibility of the talent training model. The comprehensive training of talents and the improvement of the training mode of applied talents can be realized only when the knowledge level training and the connection of knowledge content are reasonably constructed according to the corresponding talent knowledge.



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*Source: 5925433-2022-05-09.xml*