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This paper studies integrating and penetrating the concept of Civic and Political Education in the employment guidance of college students to improve the effect of employment education in the context of big data. This paper uses genetic algorithm and projection tracking method to construct a measurement and evaluation model of Civic and Political Education and employment integration level. It is found that the coupling degree between Civic and Political Education and student employment in college A has increased year by year from 2010 to 2023, showing the effective integration of Civic and Political Education and jobs. The coupling degree risen from 0.136 in 2010 to 0.987 in 2023, indicating that the penetration and integration of Civic and Political Education in employment increased significantly. Strengthening the integration of Civic and Political Education and Employment Guidance can help improve the quality of student employment and student employment satisfaction. This study provides empirical support for integrating civic and political education and employment guidance in colleges. It confirms the importance and effectiveness of civic and political education in employment guidance in colleges and universities in the era of big data.

This paper aims to create a digital platform that uses big data to manage HR performance. Firstly, the FCM algorithm transforms the dataset according to its affiliation degree and constructs a new database. An objective function is established to optimize the classification of organizational information using the correlation between data. Finally, the AHP algorithm is introduced to set indicator weights and factor sets, making the assessment results more hierarchical. The results show that the management platform system constructed in this paper operates stably, keeping the departure rate of university staff at about 5%, and 92% of students believe that the platform can improve teachers’ teaching quality. This shows that big data technology optimizes human resources management in colleges and universities.

In this paper, a digital education management information system (e-MIS) is constructed based on big data, which realizes the comprehensive processing of education administration and school management data. Through the fuzzy comprehensive evaluation model, the e-MIS system was evaluated and optimized. Then, the e-MIS system was practically applied to the fields of teaching effectiveness evaluation, teachers’ work assessment, scholarship evaluation and dormitory management. Finally, the significant impact of the e-MIS system on the enhancement aspects of new student enrollment education was empirically verified. The results show that the average score of freshmen’s school rules and discipline education is 4.35, learning culture education is 4.53, mental health education is 4.41, and life guidance education is 4.31, all of which are in the middle-upper level, as calculated by the e-EMIS evaluation model. This paper is a positive exploration of improving the efficiency of student education and management in local colleges and universities and promoting the overall development of students.

The purpose of this paper is to investigate the role of critical discourse analysis theory in the context of big data to enlighten university English teaching. To this end, this paper conducts big data mining on university English teaching under the K-nearest neighbor classification algorithm based on the optimization of K-value selection strategy. With the help of the three-dimensional discourse analysis framework under the critical discourse analysis theory, students' evaluation of the current university English in terms of learning value, teaching organization, teaching interaction, teaching coverage, and teacher-student relationship is explored. In terms of the evaluation of learning value, the students' ratings, in descending order, were: inspiring ideas, increasing insight, learning meaningful content, and learning methods. The mean scores were 4.35, 3.81, 3.68, and 3.14, respectively, and for the evaluation of teaching interaction, students thought that the teacher did the best job of encouraging speech, giving an average score of 3.38. This shows that college English teaching should adapt to the development of the times, strengthen students' comprehensive communication skills, and deepen their understanding of English culture.

At present, due to the limitations of technology and resources, the comprehensive budget management generally has problems such as imperfect foundation of establishment, weak binding force of execution, and lack of performance appraisal system. These problems become more prominent in the context of the era of big data. The use of big data technology, such as large capacity, real-time control, intelligent Settings and other characteristics, will be able to improve the current many problems. Therefore, this paper focuses on how to combine big data with comprehensive budget management. This paper holds that it is not enough for comprehensive budget management to stay in the current stage, but also need to use big data technology, which is a higher level of information stage. Therefore, this paper adopts the method of double subordination fuzzy support vector machine to study the effect of budget management of scientific research projects in Chinese universities. Based on the construction of comprehensive budget management performance evaluation index system throughout the whole process of college budget cycle, taking S university as an example, this paper analyzes the performance of comprehensive budget management in S university, and points out that big data can optimize and innovate the budget management mode, which has great significance for college budget management.

Humanities geography field practice is an important part of teaching, and this paper aims to study the design and practice of humanities geography field teaching in the era of big data. Starting from the application scenario of big data in geography teaching, this paper optimizes the GMM data clustering analysis by using the EM algorithm and analyzes the humanistic phenomena in Zhaotong City, Yunnan Province, based on the EM-GMM model, especially the rural economy and agricultural development of Zhaotong City, and then optimizes the field teaching design and practice based on the solid data support. From 2016 to 2020, the gross agricultural product of Zhaotong City increased by 4.40%, 9.62%, 14.86%, and 16.56% year by year, while the average annual decrease of crop sown area was 12,200 ha. At the same time, forestry, pastoralism, and fisheries, in general, are growing, with average annual growth rates of 17.18%, 7.95%, and 25.26%, respectively. Based on big data, we can establish a comprehensive and accurate data-based model of human phenomena in the study area to scientifically set up field practice routes, observation targets, and practice directions, which will help improve the effectiveness of students’ field practice.

In recent years, with the improvement of computers, automation, and communication technologies, autonomous driving has developed rapidly and has become a research hotspot in transportation. In order to optimize the existing autonomous driving scheme, this paper investigates the key technologies in 5G-based Telematics autonomous driving, mainly including the millimeter wave communication method and automatic obstacle avoidance strategy design, and tests and analyzes them through simulation experiments. In the simulation experiment, the synchronization rate of rear vehicle 1 of lane X1 is 97.56%, that of rear vehicle X2 is 98.43%, and that of rear vehicle X3 is 97.82%, with an average synchronization rate of 97.94%. The synchronization rates of rear vehicle Y1, Y2 and Y3 of lane 2 are 98.27%, 97.84%, and 96.89%, respectively, with an average synchronization rate of 97.67%. For the local observation latency in Telematics, the 5G Big Data-based scheme reduces 10.22% on average compared to the F-DDQL scheme and 9.76% on average compared to the IF-DDQL scheme. Regarding system latency, the 5G Big Data-based scheme reduces 8.67% and 9.21% on average compared to the other two schemes, respectively. The 5G Big Data-based Telematics autopilot can significantly improve the synchronization rate of vehicles and effectively reduce network latency. The research on the key technologies of 5G big data-based connected vehicle autonomous driving in this paper can overcome the shortcomings of traditional autonomous driving technology with unstable networking and help reduce the reliance on high-precision sensors, thus further improving autonomous driving performance.

Under the progress trend of the information technology period environment of deepening study curriculum reform, personalized learning in specialized education of industrial colleges has become one of its key contents. This paper mainly studies the necessary system design based on characteristics of genetic calculation for specialized education in industrial colleges under industry-education integration. This paper first introduces the principle of genetic algorithm. It then proceeds to introduce in detail the shortcomings of The advantage of this calculation method is that the standard calculation tendency is unable to move forward in an area extremes, the iteration is relatively slow, and the low accuracy when dealing with optimization problems. Then the design and construction ideas of the personalized platform and the database design from Learners, educators (teachers), and administrators are compared by experimentally analyzing the data diversity and convergence of the three algorithms on the database. Experimentally, the diversity of the self-adaptive genetic algorithm is maintained at 0.8, and the diversity, iteration time, and convergence standard are better compared with the comparison subject. Using a self-adaptive genetic algorithm to evaluate and rank learners’ personality differences and to provide and recommend suitable learning methods for learners helps develop education and improve teaching quality and has great historical significance for China’s development and international status.

With the gradual improvement of people’s living standards, the requirements for a living environment are getting higher and higher. A good landscape space can facilitate people’s life, promote people’s interaction, provide places for leisure and entertainment, etc. Therefore, it is important to study the landscape space of residential communities. In this paper, we use Cite space software to analyze the research on landscape space literature in recent years, use keyword search to analyze people’s preference and demand for landscape space in the community, and use the data mining technology of plain Bayesian model to analyze the existing facilities and functions of the old residential community, to explore people’s preference, demand for landscape space, people’s activities in landscape space and The design strategies for the renewal of landscape spaces in old neighborhoods are proposed. For different types of landscape spaces, walking activity is one of the main activities, and environmental facilities are the main influencing factor to determine the type of activity. The highest proportion of sitting facilities is 24.6%, showing that people need them first to stay in the space. The proportion of green facilities is 19.8%, indicating that a good green environment is also an important factor that people are willing to stay. The proportion of landscape and green facilities is close to each other, and the attractiveness to people is similar. Waterscape is also the form of landscape that people love. Therefore, green landscape is an important factor to be considered in the design of landscape space, followed by rest facilities. Comfortable and pleasant community landscape design can enhance the happiness of people’s lives, and the contents discussed in this paper are of great significance to the design of landscape space.

With the continuous development of information technology, we are now in an era of information explosion, and we are also facing the problem of information overload, i.e., it is difficult to find the news that interests us from the huge news sources on the Internet. Based on this problem, this paper proposes a perceptron model-based news recommendation system, which uses the perceptron model to extract news keywords and realize the classification of the news; for user interest preferences, we analyze users’ news browsing preferences by collecting user behavior logs and use a multi-layer perceptron model to aggregate users’ interest features. The perceptron model is compared with traditional recommendation methods in terms of accuracy and recall, and one user is selected from 50 users who have been using the system for three months to analyze their news browsing preferences by single-day, short-term and long-term news data to verify the effectiveness of the perceptron model in aggregating users’ interest features. In terms of accuracy and recall, the accuracy of the perceptron model is higher than that of the traditional method for each news type, improving the accuracy by at least 5%-10%, e.g., 10% for real estate news and 9% for financial news. The recall rate of the perceptron model is improved by more than 6% for all news types except for social and entertainment news types. In terms of user interest preference mining, the whole change process of user browsing preference trend is in line with the human forgetting curve, which verifies that the perceptron model in the system is better used and meets the design requirements. Therefore, the applicability and reliability of the model proposed in this paper for news recommendation systems are verified in terms of accuracy, recall, and user interest preference mining.

Under the wave of big data technology development, the traditional experimental teaching methods in colleges and universities are difficult to meet the requirements of the educational objectives of the times, and teaching innovation is the focus of teaching research in many colleges and universities, which has attracted the attention of the majority of educational researchers. This paper focuses on the innovation of the construction of experimental teaching platforms of economic management in colleges and universities based on the background of big data. This paper explains the necessity of innovation of economic management experimental teaching platform, proposes the basic framework of innovation of economic management experimental teaching platform (Kube storage cloud data, front-end design, collaborative filtering algorithm), and researches and discusses the innovation mode and operation mechanism of economic management experimental teaching platform based on experimental teaching construction, based on the similarity between the specified time point and other time points The impact of the time factor on the recommendation results of experimental resources is analyzed. The results of experimental teaching resources recommendation reflect the resource recommendation ability of this platform and compare with H5 technology experimental teaching platform and content analysis method experimental platform to analyze the changing trend of MAE of teaching resources recommendation; analyze the number of concurrent connections and clicks of this platform to verify the performance of this platform. The research results show that this paper platform’s experimental teaching resources recommendation effect with MAE value of 0.51 is better than other platforms. The reason is that this paper platform’s experimental teaching resources recommendation fully takes into account the time factor and co-rated experimental teaching resources, so the accuracy of experimental teaching resources recommendation is significantly improved. This study can improve the quality and teaching efficiency of universities and train more excellent economic management talents for the country, which is of great historical significance to the development of China’s economy and education.

Presently, the teaching of interior design in most higher education institutions still uses more traditional and conservative teaching methods, which to some extent, hinders the reform of interior design teaching in the era of big data. Interior design originally required students to grasp the operation fully and use CAD, 3D, and other design software, which makes the operation of complex commands, and model construction diversity, difficult for students to learn. This situation still uses the traditional lecture-based indoctrination teaching can play a very small role. This paper first focuses on the field of interior design education, based on the background of the times, provides a deep overview of the current situation of the relevant research involved in interior design, and explains the purpose, ideas, content, and methods of this paper. Secondly, we use data mining technology to build a smart classroom to visually present interior design. Finally, a comparison experiment between the traditional teaching mode of interior design and the smart teaching mode of big data is carried out, and three groups of experimental subjects are investigated after nearly one month of teaching experience. The results of this study show that the big data wisdom teaching model proposed in this paper compared with the traditional teaching model, through the one-month teaching experience post-test results, the average increase of 7 points in school A, 11 points in school K, and 14 points in school Z. The wisdom teaching system based on big data built in this paper enhanced the relevance and initiative of interior design students’ learning, improved students’ interest in learning and classroom efficiency and promoted students’ comprehensive ability while playing a positive and active role.

Teaching intervention decision is the key to realizing accurate teaching intervention based on big data. This paper portrays students’ English listening learning status and characteristics based on the analysis of students’ behavioral performance data and accordingly provides English listening teaching interventions for different students at the appropriate time. The paper also addresses the problem that the existing big data-based precise teaching interventions are not dynamic enough, introduces reinforcement learning into the teaching intervention decision, and builds a reinforcement learning-based teaching intervention decision model. The mean score of students in the experimental class was 36.31 with a standard deviation of 11.03 on the pre-test and 39.79 with a standard deviation of 6.14 on the post-test, and the mean score of students in the control class was 36.5 with a standard deviation of 10.15 on the pre-test and 36.13 with a standard deviation of 6.14 on the post-test. The difference between the experimental and control classes’ mean scores was only 0.18 on the pre-test and 3.66 on the post-test. Therefore, the precision teaching intervention framework based on big data constructed in this paper has achieved better results in English listening teaching and has strong applicability.

China is moving forward, but it pays much attention to data analysis of infinite resource circulation. Therefore, this paper investigates the time-domain convolutional calculation method of deep learning and recurrent calculation method of deep learning and uses them to study and analyze the obstacles and countermeasures for the share of renewable energy in our country’s right grid. Then, the passing situation of renewable energy in China is analyzed. Information related to China’s advancement and utilization of inexhaustible resources from 2010 to 2020. From the analysis of the gathered information, it is unequivocal that the scale and share of inexhaustible resources of ancestors in our country’s rights are gradually increasing. Space occupancy in recent 3 years of inexhaustible resources generation in our country will be more than 133 million kilowatts, and the proportion is about 70% of our country’s right power supply; the solar power generation capacity will increase from almost nothing to 261.1 billion kilowatt-hours in 2020 from less than 300 million kilowatt-hours in 2010, which is nearly exponential growth. Finally, this paper combines the existing features of renewable energy in China, puts forward the problems and challenges encountered in the advancement of inexhaustible resources, and gives countermeasures that can be strengthened the utilization rate of renewable energy in the power grid.

With the development of big data technology, not only driving the development of the social economy but also the news media industry is developing in the direction of integration and innovation, and promoting the dissemination of news through data value factors is the focus of current research. This paper takes data news as the research object, takes the framework theory as the entry point, and mainly studies the data news production dilemma and its optimization path. Firstly, the data news information is classified by entity extraction, and the weights between the entity information are calculated to establish the association. Secondly, the IE-Page Rank algorithm is proposed to get the IER value of each information entity by iterative calculation, which is used to identify its importance and quantitatively get the importance ranking of all information entities. Finally, the basic framework of data news visualization is constructed, and the applicable visualization optimization dissemination path is given in the case. The research results show that compared with the traditional media news dissemination model, the improved data visualization dissemination model increases efficiency by 32.3%, timeliness by 18.9%, user satisfaction by 21.1%, and effectively increases the reading volume and dissemination paths by 17.2% of users. The improved data news visualization dissemination model proposed in this paper improves the professionalization of data analysis, enhances the interactivity and visualization of data news works, and provides guidance for disseminating data news.

With the development of big data, virtual course teaching has developed rapidly. In virtual teaching, the lecturer’s position is pivotal, and the virtual teacher’s lecture mainly conveys information through intuitive facial expressions, so the effectiveness of facial synthesis is crucial. However, traditional facial expression synthesis methods suffer from local distortion and lack of subtle expressions due to driving complex topological models and delays due to large computational effort. To this end, this paper proposes a facial expression extraction method based on feature point texture mapping. First, a video face expression feature point tracking algorithm is designed. Firstly, we design a video face expression feature point tracking algorithm. We use a monocular camera to capture facial expression images, use a texture mapping algorithm to detect faces and extract face feature points, and add a time threshold processing mechanism to the relevant filtering algorithm to detect face frame tracking to realize face feature point tracking in video. Secondly, the parameters are controlled by face expression animation, including head pose and face expression parameters. The head pose is solved by the Laplace algorithm, and the head rotation matrix and translation vector are output to establish the coordinate system. Finally, the stability and real-time performance of the feature point texture mapping algorithm are verified by expression capture test and recognition effect. The results of this paper show that: in the expression capture test, the average data transmission time is about 1ms, and the total expression transmission time of each video image frame is 32ms, which is 1.23ms and 9.8ms shorter than the traditional algorithm, respectively; in the expression recognition effect test, the successful recognition rate is increased by 10.2% on average compared with the traditional algorithm; and in the application to the classroom teaching effect test, 86.6% of the students are satisfied with the virtual teacher. 86.6% of the students highly agreed with the virtual teacher teaching, and 83.3% of the students thought that the model had a positive effect on the learners’ learning, which proved the superiority, real-time, stability and practicality of the algorithm designed in this paper compared with the traditional algorithm. The research work in this paper also provides solutions to the problems of virtual course teaching.

With the continuous development of the economy and the level of science and technology, people are no longer satisfied with the old rural tourism model, and information technology is developing rapidly, which brings an opportunity for developing rural tourism innovation. As a product of the comprehensive development of the information age in recent years, “Internet+Rural Tourism” builds an intelligent cloud platform for rural information services relying on big data and other advantages and establishes a rural intelligent tourism service system to provide convenient, intelligent, and diversified services for tourists. This paper elaborates on the practical significance of “Internet + rural tourism” development opportunities”. It proposes to scientifically calculate the environmental capacity of rural eco-tourism areas, control the scale of visitors and reasonably invest in costs because of the problems of lagging infrastructure facilities and limited distribution of inputs and outputs in the process of its development. The results of this paper show that the satisfaction level of rural tourism tourists is the lowest, with the degree of “dissatisfaction” as high as 38.40%; in the rating of rural tourism indicators, the rating of rural ecological environment is only 52.41, the average rating of rural economic indicators is 60.12, and the average rating of comprehensive management indicators is 58.2. The results of this paper also illustrate that the current rural tourism development has three major problems: environment, economy, and management and that rural tourism should not blindly invest high costs if it wants to seek high-quality development but should seek long-term development of rural tourism based on its characteristics and rural ecological environment indicators.

Ethnic music education in college music education is crucial to transmitting ethnic music culture. Based on the K-means algorithm and XGBoost algorithm in big data algorithm, this paper begins with the concept of folk music as “culture” and the theoretical basis of folk music education in colleges and universities as a preliminary theoretical preparation for the research. Through a questionnaire survey and data analysis of music majors and teachers in several colleges and universities in Liaoning Province that offer “folk music” courses, we summarize and analyze the current problems in teaching folk music and propose countermeasures for cultural inheritance. The analysis shows that people generally prefer popular music, and their liking for folk music is generally low, among which only Shenyang Normal College has more students who like folk music, 61%, and the least is Bohai University, only 4%. At the end of this paper, three corresponding reference countermeasures are proposed, including the establishment of multicultural awareness, a scientific and open concept, and the principles of folk music culture inheritance, which have implications for the inheritance of folk music culture.

This paper aims to study how to innovate and develop the culture of the old industrial base of Northeast China in the environment of big data. In this paper, based on sorting out the big data processing process, a data mining model based on the Bayesian network is established, parameter learning is performed using great likelihood estimation, and the Bayes-Dilley scoring function is obtained by structure learning. Then, based on the trained network, the big data mining analysis is performed on the cultural industry of the old industrial base of northeast China and the northeast China cultural impression words on social networks. From 2018 to 2021, the successive annual growth values of the cultural industry of the old industrial base of northeast China are 119.835 billion yuan, 120.345 billion yuan, 115.764 billion yuan, 116.001 billion yuan, and the proportion of GDP increased from 2.11% raised to 2.38%. Among them, Jilin was raised from 2.14% to 2.46%, Heilongjiang from 2.65% to 2.72%, and Liaoning from 1.03% to 1.52%. The cultural innovation of the old industrial base in Northeast China under the environment of big data should abandon the traditional culture, find the cultural positioning, break through the thinking stereotype, create a new advanced culture, and change the ideology.

Electric power enterprises are developing rapidly in the era of big data information digitization. At this stage, the total number of substations is gradually increasing, the structure of the power engineering system is slowly becoming complicated, and the video monitoring system instantly collects a lot and contains a lot of noisy data information, which affects the power supply system’s access to effective data information and fault detection. To prevent the above phenomenon. This paper selects a decision tree algorithm to obtain and analyze meaningful operation-confirming information from a large amount of data information, and then can quickly and confirm the diagnosis of common fault machines and equipment in substations, reduce the running time of common fault machines, and improve the safety and reliability of primary equipment in substations with automation technology. The paper describes the basic concept of big data mining common algorithm and its data mining algorithm in the automation technology substation primary equipment fault detection, selected the typical alarm signal to start the analysis, and categorization and collocation solution. A decision tree algorithm entity model is built, several classical decision tree algorithms are described, and their data analysis is carried out for each attribute, and then the decision tree algorithm is improved. According to build the decision tree algorithm according to improve the decision tree algorithm under the fuzzy set base theory, mainly by expertise in the four on cannot identify the association, rough set and up close and down close, similar and membership relationship, expertise concise to optimize the calculation method. And the common ID3, C4.5, and CRAT algorithm of each property is compared and analyzed, and the results show that: compared with C4.5 and ID3, the boosted optimization algorithm has higher classification accuracy and can model rate more quickly. The research in this paper can quickly diagnose the automation technology substation primary equipment and fault phenomena, and its establishment of the whole process is easy, the scope of application is relatively high, and it has wide applicability.

Bridge joint bearing capacity analysis is difficult to fit the requirements of the era of big data. The traditional way of bridge-bearing capacity numerical analysis exists in the wave of digital development of big data information, the Internet of Things, and artificial intelligence technology like a wild development. The traditional way of under the problem: is efficiency, engineering analysis is not accurate, data information paper, work responsibility is not in place, technical equipment, etc. This paper revolves around the numerical simulation analysis of the load-bearing capacity at the joints of symmetrical cantilever-assembled bridges based on the logistic regression model by finding literature to understand the concept and algorithm of the logistic regression model, construct logistic regression model based on dichotomous dependent variable logistic regression algorithm, logistic regression model parameter estimation, logistic regression. The three aspects of the model evaluation index are studied. The numerical simulation analysis of the load-bearing capacity at the symmetrical cantilever joint based on the logistic regression model, the shear strength calculation at the bridge joint, and the bridge longitudinal bar slip deformation are calculated, respectively, and then the actual value of the bridge load bearing capacity is derived. Simulation in the bridge bearing capacity at 0kN, 5kN, 10kN, 15kN, 20kN, 25kN, and 30kN, the model algorithm evaluation index under the three indicators of prediction rate, accuracy, deviation of the verification analysis. The results show that the prediction rate of the logistic regression model algorithm based on the load carrying capacity up to 30kN is 84%, the accuracy rate is 94%, and the deviation is 11%, and the three index values have better performance compared with the one-dimensional linear regression model algorithm and the multiple linear regression model algorithms. This study is beneficial to extend the bridge structural analysis function and make the bridge load capacity assessment more automatic; it can facilitate the comparison of historical data of bridge tests, longitudinal change of indicators at different testing time points, and grasp the current situation and change trend of bridges. Thus it is of great historical significance to the development of the Chinese bridge infrastructure industry.

The current employment situation of graduates is serious and complicated, and employment work is very difficult. Furthermore, the employment quality of college graduates is an important reflection of the quality of education teaching and talent cultivation in higher education. Using big data to provide all-round and multi-perspective feedback on employment quality can find the direction of efforts to improve employment quality further, provide an important reference basis for schools to adjust the layout of majors, make development plans, reform education teaching, optimize career guidance courses and optimize enrollment policies and measures, and effectively promote universities to fulfill better their historical mission of serving the society. This paper extracts features for a series of school behavior data of students and carries out feature selection through appropriate methods. Specifically, the construction problem of the traditional random forest model is analyzed, and a new model is proposed by combining it with a decision tree algorithm; then, the model is constructed and adjusted by grid parameters to ensure the accuracy of the model, and finally, the model is compared and analyzed. The research shows that the accuracy of the RF prediction model using standard features is 0.702, and the LSTM prediction model using only temporal features is only 0.683. In contrast, the prediction accuracy of the model in this paper reaches 0.805. Therefore, the prediction accuracy of the model based on the decision tree algorithm is much higher than the other two, which is effective for optimizing the teaching of career development and employment guidance courses in colleges and universities, thus can improve the education and management-related to career planning and career guidance in schools.

Customer feedback on online platforms is an unstructured database of growing importance for organizations, which, together with the rise of Natural Language Processing algorithms, is increasingly present when making decisions. In this paper, a bibliometric analysis is carried out with the intention of understanding the prevailing state of research about the adoption of sentiment analysis methods in organizations when making decisions. It is also a goal to comprehend which business sectors, and areas within the company, they are most applied, and to identify what future challenges that in this area may arise, as well as the main topics, authors, articles, countries, and universities most influential in the scientific literature. To this end, a total of 101 articles have been gathered from the Scopus and Clarivate Analytics Web of Science (WoS) databases, of which 85 were selected for analysis using the Bibliometrix tool. This study highlights the growing popularity of sentiment analysis methods combined with Multicriteria Decision Making and predictive algorithms. Twitter and Amazon are commonly used data sources, with applications across multiple sectors (supply chain, financial, etc.). Sentiment analysis enhances decision-making and promotes customer-centric approaches.

Music, as a carrier of emotional sustenance, can not only achieve spiritual resonance in the process of listening but also reflect the vitality of life in the rhythm of music. As an effective relief method, music intervention has far-reaching significance in the development of psychotherapy. In the practice of music therapy, music selection is particularly important, and the rationality and rigor of music selection directly affect the therapeutic effect. The process of music selection in music therapy is in common with Internet music recommendation, so it has theoretical and practical value to apply personalized music recommendation algorithms to music therapy. In this paper, driven by big data, a music recommendation model based on an improved collaborative filtering (CF) algorithm is proposed, which combines the psychological adjustment of users’ music preferences and different music rhythm features to select music for music therapy and provides theoretical support for music therapy selection. The results show that the construction of music resources for music therapy based on an improved CF algorithm can greatly improve the music selection process of music therapy.

English teaching in colleges and universities is facing the growth of personalized teaching demand, and the traditional education model is challenging to meet the diverse learning needs of students. Big data and decision tree technology provide a new way for teaching content optimization, which can achieve a high degree of matching between educational resources and students’ needs, and improve teaching effectiveness and learning efficiency. This study explores the application of decision trees and big data technology in optimizing English teaching content in colleges and universities. By constructing an ontology tree model of learning resources and adopting similarity calculation and personalized recommendation algorithms, the study achieves personalized learning resource recommendations for students. The study utilizes comparative experimental methods to analyze achievement changes between the experimental and control classes after customized learning. The personalized teaching program’s effectiveness can be confirmed by the fact that the average final grade of students in the experimental class is 1.5 points higher than that of the control class. The Analysis of CET-4 scores found that personalized teaching improved students’ English listening, reading, and writing skills. This paper combines decision trees and big data technology to optimize teaching content, effectively enhancing students’ English learning outcomes and providing new teaching strategies for English education in colleges and universities.

In this paper, we study the big data multi-strategy predator algorithm for tourist flow prediction and explore the application of the algorithm in optimizing the tourist flow prediction model to improve the prediction accuracy and efficiency. An adversarial learning strategy extends the search space, an adaptive weighting factor balances the global and local search ability, and a variance operation combined with differential evolution is used to avoid local optimal traps. The experiment adopts variables such as network booking volume and search index as inputs for passenger flow prediction. The predator algorithm is trained by Extreme Learning Machine (ELM) to optimize the input weights and biases to build the FMMPAELM model. The results show that on the training samples, the FMMPA-ELM model predictions are highly consistent with the actual values, with a maximum prediction index of 200.On the test samples, although there are errors, the FMMPA-ELM model exhibits better prediction ability than the traditional ELM model. It is concluded that the FMMPAELM model can effectively improve the accuracy of passenger flow prediction and provide powerful decision support for the tourism industry.

This paper discusses the application of big data in community corrections and analyzes its contribution to improving the effectiveness of governance, focusing on enhancing the science and effectiveness of community corrections strategies. Using a Random Forest Model and Deep Reinforcement Learning Network, the dangerousness of community correctional officers is analyzed and corresponding strategies are proposed. The random forest model achieved 99.92% accuracy in predicting community correctional officers’ delinquency, and 99.79% in predicting recidivism. The deep reinforcement learning model performs well in correctional strategy recommendations, with an accuracy rate of more than 90% for different strategies. The application of big data technology effectively improves the formulation and implementation of community correctional strategies, which positively impacts modernizing social governance.

The development of Japanese language education provides a solid foundation for the friendly relations between China and Japan. In this paper, we first constructed a framework for optimizing teaching in Japanese language education based on big data and intensive learning intervention decision-making. We designed an achievement prediction model based on deep learning for Japanese language teaching effectiveness. The achievement prediction model is applied to the teaching achievement prediction and compared with the actual achievement, and its relative error range is between 0~0.2, compared with the traditional prediction model error reduction of about 0.2 or so in the Japanese language teaching achievement prediction analysis, the confidence and validity of the learning law and class performance are more significant than 0.8, the association rule is superior, and the optimized student prediction of the Japanese language teaching course learning achievement is basically distributed between 80 and 100 points, and the achievement level was significantly improved. This study provides an effective method for the precise reform of Japanese language teaching and realizes the prediction of the effectiveness of educational reform.

College students’ entrepreneurship and employment have been considered an important factor in the country’s social and economic development. This paper firstly establishes a multivariate linear regression model, uses the least squares method and the great likelihood estimation method to estimate the parameters of the model, and uses the T-test and F-test to test the model. Then, we take the employment and entrepreneurship level of students in 10 colleges and universities in X urban area as an example to explore the feelings and reactions to the pressure of employment and entrepreneurship of college students and dig out the challenging factors faced by students’ employment and entrepreneurship. Finally, the regression model was used to test the influence of each factor on college students’ entrepreneurship and employment and to explore the cultivation path of college students’ employment and entrepreneurship ability enhancement. The raw mean score of college students’ employment and entrepreneurship pressure feeling is 3.046±0.418, and the explanatory strength of entrepreneurial environment and personal traits on employment and entrepreneurship intention is 43.7% and 37.7%, respectively, which indicates that personal traits and employment and entrepreneurship environment have a significant influence on college students’ employment and entrepreneurship. This study helps to promote the reform of innovation and entrepreneurship education in colleges and universities, improve the talent cultivation mechanism, and provide a positive reference value for the research on the cultivation of employment and entrepreneurship ability of college students.

This paper analyzes the characteristics of university cyberspace ideology in the context of big data and the causes of risk formation and designs the index system of university cyberspace ideology governance at three levels: micro level, meso level and macro level. The combination of hierarchical analysis and entropy method is used to determine the weight of each evaluation index and establish the evaluation model of governance performance based on gray correlation analysis. After calculating the weights of ideological security in college cyberspace are 0.58, 0.52, 0.55, 0.55 and 0.53, the evaluation result of the constructed college cyberspace ideology governance system is 0.28, which is good.

The development of the data era has put higher requirements for college art courses, but the imperfect evaluation system of practical teaching of college art courses has become a stumbling block to the flourishing development of college art. Based on the decision tree model, this paper proposes constructing the optimal decision tree using pruning techniques. The optimized decision tree is adopted to explore the differences in the construction of art majors in universities under the condition of normal major certification and feedback on the influence of normal major certification on art majors through the evaluation results. From the analysis of the three index examples, the mean values of cognitive evaluation, pedagogical evaluation, and effectiveness evaluation of art courses were 2.527, 4.756, and 3.267, respectively, indicating the best evaluation in the pedagogical evaluation of art courses, but the cognitive evaluation was slightly lower. Normal major certification significantly influences the construction of art majors in colleges and universities. The decision tree model can also guide the innovation direction of college art courses more intuitively through data so that the evaluation system of college art courses can enjoy the development dividend of the new technology era.

This paper researches the teaching reform of the integration of sports and civic politics in the new era, and the addition of sports elements in civic politics education can achieve the mutual integration of the two. A personalized Civic and Political teaching model is constructed by combining the attention mechanism based on a recurrent neural network. Based on the personalized teaching model, a model of students’ academic ability in Civics and Politics is established. The EERNN model predicts the next Civics and Politics test scores and assists teachers in proactively intervening. The path of sports Civics integration is proposed from three aspects: increasing the implementation of sports Civics education, improving the awareness of Civics education of physical education teachers, and supervising the integration of students’ sports Civics thinking. Set up an experiment that compares the cognition of students in the experimental and control classes on health indicators and Civics elements. Analyze the student’s learning behaviors and the results after using the personalized recommendation Civics teaching model. The results of the experiment show that the students’ Civics elements of the seven dimensions improved after the experiment, respectively, by 3.05, 2.17, 3.79, 2.16, 2.67, 3.24, and 2.77. The Civics scores of the students in the experimental class increased from 113.89 to 123.48. The personalized learning model effectively enhanced the effectiveness of students’ civics learning.

In this paper, we first constructed a college education management system based on big data technology, clarified the system layout, analyzed the functional requirements of different personnel roles, and used the B/S development structure and mode to complete the layout and construction of hardware. Then, the evaluation model is established by using factor analysis and principal component analysis to calculate the eigenvalue, variance contribution rate and comprehensive score of each factor, and the evaluation index system is established. Finally, the effect of digital education management in colleges and universities is evaluated in terms of reliability, responsiveness, assurance and tangibility, and overall perceived quality. The scores of all dimensions are higher than 4, the student’s expectations are all in the range of 5-6.5, and the real perception is in the range of 4-5.5. Teachers’ expectations were in the range of 5-6 points, and their true perceptions were in the range of 4-5 points. Teachers and students are satisfied with the effectiveness of school management. This study has significance for educational reform as well as management upgrading in colleges and universities.

This paper first clarifies the goal of English precision teaching, constructs a big data precision teaching model by combining it with Internet technology, and designs a precision teaching flow chart. Then the regression model is used to construct the assessment model of the precise teaching program, portraying the relationship between the teaching effect and students’ characteristics and behavioral performance under the teaching program. Then, the parameters of the assessment model were evaluated using a similar least squares method to optimize the model. Finally, the assessment model was used to analyze the level and impact of English precision teaching. The results show that there is a significant improvement in students’ performance after precision teaching p=0.00

In this paper, firstly, the Chinese-English news corpus is constructed using crawler technology, and a distributed retrieval system is designed to search the Chinese-English data in the corpus. Secondly, the potential semantic analysis algorithm is improved to extract semantic information from the Chinese-English corpus and realize the semantic search data source. Finally, by designing the experimental environment as well as the samples of the Chinese-English news corpus, the performance of the corpus as well as the distribution retrieval performance are analyzed, and the comparison of Chinese-English news metaphors is explored. The results show that the system’s word-level accuracy is 96.32% when the training corpus size reaches 100,000 word-level, the F-value is 96.08, and the latency of 1Mb/s is higher than the latency of 3Mb/s and the latency time of 5M/s, and the corresponding latency is always stabilized within 1.0. The proportion of conceptual metaphors in Chinese news is above 0.5, while in English news, the proportion is mostly around 0.3. Most of the Chinese news is conceptual metaphors, while most English news is interpersonal metaphors.

With the development of smart grids, power grids have accumulated massive amounts of data in various links such as power generation, transmission, substation, distribution, power consumption, and dispatch. More and more big data applications are beginning to be applied in various professional fields of the power grid. Promote the application and value discovery of smart grid big data through data fusion inside and outside the grid. Grid data has become an important asset for enterprise development, but power grid enterprises lack effective technical means to solve the whole life cycle monitoring and relationship of power grid data assets. Aiming at the relationship between power grid data assets, this paper proposes a set of grid data asset relationship and intelligent classification framework that integrates knowledge graph and Internet of Things. First, the grid knowledge graph extraction relationship is carried out by ProjE algorithm. Then, the relationship between power grid data assets and intelligent classification framework that integrates knowledge graph and Internet is proposed. Finally, the corresponding classification application is proposed by using intelligent classification algorithm. Experimental results show that the intelligent classification accuracy rate can reach 93.12% under the relationship between the knowledge graph and the Internet data assets, which has a new idea for the future development of the relationship between power grid data assets.

With the improvement of information technology, all industries have developed rapidly. However, the education industry is lagging behind in the process of informationization. Most still follow the educational paradigm of the past, where teachers rely on personal experience to make judgments and teaching decisions for students, just like a blind man feeling like an elephant. In this paper, we design a teaching quality monitoring and evaluation strategy according to the K-modes algorithm in big data technology, which is a dynamic data collection and intelligent analysis based on the learning process and learning effectiveness, ensuring data authenticity and also realizing multi-dimensional data collection and multi-angle evaluation and analysis, and finally comparing each index of three different algorithms. It is verified that the minimum error sum of squares of the optimized K-modes algorithm is 711, and the correct rate is 0.97, while the values of this metric for the other two algorithms are 1587 and 986, and the correct rates are 0.91 and 0.92, respectively. Therefore, the designed mechanism is a system with a superior evaluation effect and a feasible prediction model for faculty development in local vocational colleges.

With the rapid development of big data Internet technology in recent years, the national fitness exercise and dance competition have flourished under the strategic background of national fitness, and the scale of the event, as well as the number of participants, has grown rapidly, and the country has begun to make reasonable adjustments to the construction of sports culture and has attached importance to the reform and innovation of the work of sports culture construction. This paper uses the literature method, logical analysis method, and other research methods to analyze the position of the national fitness exercise and dance through SWOT, and formulate the marketing strategy of the national fitness exercise and dance event, to provide some theoretical reference basis for the relevant event organizers. With the marketing strategy of national fitness exercise and dance events as the theoretical basis, the hierarchical structure of the big data platform of sports culture construction system and the logical structure of the sports data warehouse are analyzed and established through the support of big data Internet technology, and finally, the data model of sports culture construction platform based on big data Internet technology is constructed. The marketing strategy of national fitness exercise and dance events is a brand new marketing model built on big data technology and modern sports marketing theory. With the development of big data Internet technology, national fitness exercise and dance event marketing will certainly bring a positive impact on the audience and promotion channels of fitness exercise events. The scope of participation expands year by year, the number of participants expands year by year, the number of participants rises year by year, etc., thus promoting the healthy and rapid development of national fitness exercise and dance event marketing and sports culture construction plays a positive role.

The background of big data has developed deeply, the application of the field has been broadened, and the value of data has been vigorously manifested. In order to study the interaction between physical culture education and exercise in universities in this era, this paper uses Clementine 12.0 data mining software to build a data mining model of association rules of university physical culture education courses and mine the course feature vectors. Based on the mining results, we designed the second classroom physical culture education courses with different physical culture characteristics. Constructing a scoring method and rating scale for the effectiveness of physical exercise among college students, and the physical exercise index scores are obtained through fuzzy operations. Finally, the interaction between physical culture education and exercise in universities in this context is analyzed according to the relationship between physical culture education courses and physical exercise performance. After the physical culture education course began, the physical exercise intensity score of the experimental group of first-year college girls increased by 8%, the physical exercise time score increased by 10%, the physical exercise frequency score increased by 15.2%, and the total physical exercise score increased by 7% after the physical culture education course. This shows that university physical culture education is positively correlated with college students’ physical activity, and campus physical culture has a significant predictive effect on students’ subjective performance of physical activity behavior. Optimizing university physical culture education not only improves students’ physical quality and promotes the development of their physical and mental health but also provides a reference for strengthening students’ physical education.

The dilemma of exploring the children in institutions of higher learning to find jobs or start their own companies of rural origin and return to their hometown is paving the way for college students to find jobs of rural origin. In this paper, a decision tree optimized by a partial least squares regression algorithm is proposed to construct a DT-PLS data classification model in the case of cloud data, and the performance of the DT-PLS model is evaluated for the DT-PLS model. Using college students’ employment and entrepreneurship data from colleges and universities as examples, two factors that influence college students of rural origin to return to the registered permanent residence to find a job or start a company, namely subjective factors and contextual factors, are mined, and data analysis is carried out for the two influencing factors. From the 12 sub-indicators of subjective factors, the mean values of those considered very unconformable, unconformable, uncertain, conformable and very conformable were 17.87%, 15.38%, 10.82%, 16.09% and 39.84%, respectively. Regarding the 10 sub-indicators of contextual factors, the mean values of the percentages of those considered very non-conforming, non-conforming, uncertain, conforming, and very conforming were 6.56%, 20.95%, 13.69%, 26.13%, and 32.67%, respectively. The DT-PLS classification, the main method here, analyzes the current dilemmas of returning college students of rural origin to entrepreneurship and employment and prompts more college students to invest in the grassroots to provide fresh talent blood for rural revitalization.

In the context of vocational education reform, with the rapid development of big data and internet technology in recent years, coupled with the increase of employment pressure in China, universities have paid more attention to students’ employability competitiveness and paid attention to the reform and innovation of employment education and management work around the goal of higher vocational education personnel training. Based on the theory of the GRU employment education model and supported by big data internet technology, this paper uses a questionnaire survey and semi-structured interview to conduct exploratory factor analysis and validation factor analysis on the constructed employability evaluation index system by using empirical analysis method and finally constructs an employment education model for higher vocational students. At present, compared with the nearly 100 years of exploration history of employment education in colleges and universities in developed countries such as the United States, the employment education of college students in China has a relatively short history and is still in the exploration and development stage. The combination of vocational education reform and big data and internet technology will create a good environment for the future development and growth of students. Therefore, it is necessary to improve the relevance of employment education and management from the perspective of students and teachers in the “Internet+” environment and provide some new ideas to solve the outstanding problems in the employment education of college students so as to ensure that students can enter and adapt to the workplace smoothly after graduation.

With the continuous development of information technology, especially the rapid development of big data technology, the coverage and effectiveness of information application and dissemination have increased substantially. With the current innovation and development of music management education mode, the traditional accordion course management mode also changed, this paper makes full use of big data technology to optimize the accordion course education management path, mainly through the construction of an accordion score database and retrieval system, the closer the data mining algorithm calculation result is to 1, it means that the data mining result is more similar to the actual result. The experimental analysis result is equal to 1, which indicates that the data mining result of this study is similar to the actual result, which is conducive to the intelligent management under the analysis of big data technology for music course education, improving students’ learning interest and passion, improving the teaching quality of learning, and facilitating the teaching management work of university leaders. And it can be further based on the big data era, grasp the trend of the big data era, and understand the level, learning needs, and personal characteristics of students, analysis can be seen that accordion course education management based on big data can make different learning plans for different levels of students, which is conducive to improving management quality and efficiency at the data level.

Under the background of big data development, students have diverse and multi-level demands for music aesthetic education teaching. This paper studies the implementation strategy of aesthetic education in college music teaching under the threshold of big data, mainly through data analysis, predictive modeling and simulation, multi-faceted analysis, and the design of personalized music teaching intervention. By training DNN model experimental conclusion and analysis, the DNN model in the original set R-squared reached 0.8037, the largest performance among all models, compared to multiple linear regression, in multi-level multi-neuron activation, through the sigmoid activation of each level and linear calculation between levels, in backpropagation to update the weights and bias, trained to be able to more The DNN is finally selected as the best model for music learners’ performance prediction. The DNN is finally selected as the best model for music learners’ performance prediction. The advantages of big data mining music aesthetic education resources are exploited to create an open aesthetic education teaching space for students.

Do a good job in the way of college students’ network political innovation based on big data thinking, so that it can play a stronger advantage and energy in college education. Carrying out innovative exploration of college students’ curriculum education based on big data thinking can continuously deepen the theoretical research significance of Internet political education. It can also put forward suggestions for better practice of network ideological and political education in colleges and universities, which has the dual significance of theoretical construction and practical guidance. Therefore, the MCA-sampling model is designed in this paper. According to the calculation of the sampling model, the opportunity for online ideological and political data literacy is 11%, and the challenge is 89%. This is because any flaws at any level will bring a severe test to the calculation of the effectiveness of online political teaching methods for students, which greatly increases the difficulty of online political educators. Through the horizontal comparison, it can be seen that the acceptance theory focusing on “receiver-centered” is the most innovative. Its innovativeness is 83%. The probability of timely method innovation is as high as 89%. The most unstable aspect of innovation probability is the root cause. Its probability of innovation is at least 24%.

With the development of Internet big data technology, more and more modern medium and large enterprises have started to introduce Internet technology into the internal management of enterprises, and project management is the most important application area that accompanies the growth and development of enterprises. This paper takes traditional enterprise project management as the theoretical basis, supported by big data Internet technology, through big data unstructured data classification process, using empirical analysis method to conduct an exploratory analysis of the constructed management capability index system, and finally constructs a function model based on the application of big data technology in enterprise project management. After analyzing the digital overview, the necessity of using digital in enterprise project management is clarified, and the exploration of using digital in enterprise project management is proposed. Through the design and development of resource management, operation queue, and scheduling strategy, the joint analysis of data level between multiple systems is constructed to create a good environment for enterprise project management. The development of big data Internet technology brings important and positive changes to enterprise project management, helps enterprises to keep up with the trend of the times, efficiently apply the advantages of digitalization, and promotes enterprise project management towards standardization and efficiency.

Under the overwhealing trend of big data, information literacy is gradually embedded in various applications with broader contents. Due to the new concepts and new technologies, the contents and methods of information literacy education have also changed. In order to further study the mode of information literacy education under the environment of big data, this paper takes six Hainan high school libraries as an example to investigate the students’ understanding level of information literacy education - their perception of the role of information literacy education and their satisfaction with information literacy education. In term of students’ knowledge, 28.25% of students have never heard literacy education, 37.28% have heard but are not sure of the details, and 34.47% know about information literacy education. In terms of students’ perceptions of the role of education, 48.27% thought that information literacy education was important, 48.15% thought that information literacy education was neutral, and 3.58% thought that information literacy education in libraries was not useful at all. In terms of satisfaction, students who are very satisfied, satisfied, and not satisfied with information literacy education is 33.33%, 52.12%, and 14.55% respectively. Our result showed that information literacy education in college libraries still lacks publicity, and developed mulitiple teaching model to attract student attention is necessary. Therefore, we purposed that information literacy education in college libraries should develop diversified publicity based on various media, particular follow the new characteristics of college students’ information acquisition behavior, and expand the scope of information literacy education.

In today’s era of digital development of big data information, cross-cultural and cross-regional communication are becoming more and more frequent, and human beings have the right and desire to enjoy certain common cultural information, and it has been 38 years since China and Thailand established diplomatic relations in 1975. Cultural differences in Sino-Thai translation based on big data are studied in four aspects: inter-translation of personal names, inter-translation of geographical names, inter-translation of quantity sets, and inter-translation of date and time. This paper focuses on the research of cultural differences in Sino-Thai mutual translation based on big data analysis, constructing a bilingual word translation model with monolingual word vectors under the big data platform Spark, and conducting data analysis on the accuracy and efficiency of translation by collecting Chinese dataset and Thai dataset in parallelization algorithm in two structures SchemeA and SchemeB . The results show that the accuracy of the bilingual word vectors generated by either SchemeA or SchemeB improves as the size of the dataset increases. In addition, SchemeA is better than SchemeB in terms of accuracy and SchemeB is better than SchemeA in terms of efficiency performance, so Sche A me is suitable for translation tasks with high accuracy requirements, while Sche B me is suitable for translation tasks on large data sets. This study can improve the efficiency and accuracy of mutual translation between China and Thailand and train more translators. Today’s high technology brings much convenience to people, and social knowledge is constantly updated and developed. It is necessary to study in order to keep up with the changes of the times and to grasp the updates of the words of the times in order to translate well and more accurately, and this study not only contributes to the translation work in China but also has an important historical significance to the cultural exchange and communication between China and Thailand.

In order to improve the English writing ability of college students and help them lay a good foundation for written communication in English when they graduate from school or continue their studies abroad. This paper collects data on college students’ English writing on the Internet through a clustering algorithm with the support of big data technology background and defines the scoring criteria for essays to target the deficiencies in college students’ English writing nowadays and urge students to correct them. With the support of a search algorithm, we obtained the demand for college students’ English from employers in China over the past 10 years and reflected on the aggregated demand data to link classroom teaching with workplace English. The study found that the English language skills valued by employers in order were speaking 80.5%, writing 86.3%, reading 57.9%, listening 40.2%, and translation 21.7%. A demand questionnaire survey was conducted randomly among non-English majored first- to fourth-year undergraduate students in five universities with tiered teaching. The results showed that 72% of the students thought they needed to improve their English speaking skills, 70.3% thought they needed to improve their English writing skills, and 40% thought they needed to improve their grammar skills, which shows that college students’ needs for improving their English speaking and writing skills are particularly prominent. The emphasis on English writing classroom education for college students can solve the urgent needs of students for English learning and improve college students’ English writing skills through big data science.

With the rise of artificial intelligence technology in the context of digital development of big data and information, the biosafety management method under the traditional model can hardly fit the development requirements of the times, and the biosafety management under the traditional model has various problems such as slow manual efficiency, tedious management work, and humanization of work. This paper focuses on studying a biosafety management strategy based on a multiple regression model, then analyzes and compares the two methods of the one-dimensional linear regression model and the multiple linear regression model. The study is conducted under the three indicators of biosafety management weight score. Arithmetic means to score and significance of 100 enterprises (schools, pharmaceutical companies, hospitals, and other types of these four major types) under the multiple linear regression model. The results show schools topped the list with a biosafety management weight score of 0.793. The arithmetic mean scores of the four categories of enterprises were ranked in descending order: schools, hospitals, pharmaceutical plants, and other categories. The size of the enterprise has a significant and positive effect on biosafety management, which means that the larger the size of the enterprise, the more resources the enterprise has under its control, and then the implementation of biosafety management and the improvement of its performance will be guaranteed. Analyzing the efficiency of biosafety management in hospitals based on three different methods over the past 20 years, it was found that the efficiency of biosafety management based on multiple linear regression models fluctuated from 60% to 95% compared to the other two methods. This study promotes university laboratories as important and safe bases for society to cultivate highly qualified and skilled personnel in line with the new era of production, management, and service lines and to contribute to the country’s experimental research work. This study has substantial reference value for biosafety management and promotes bioscience development in China.

China is in the critical period of “four synchronous development” of industrialization, informatization, urbanization, and agricultural modernization, so it is urgent to find a correct, scientific, and reasonable development strategy for modern agricultural products and promote the development of big data agriculture. In this paper, we use big data technology to determine the algorithm model of agricultural big data technology and the application system of the agricultural Internet of Things and argue for big data for agricultural planting technology, agricultural economic management, and agricultural industry upgrading in order to find the optimal strategy for the development of modern agriculture in China. As of the statistics at the end of 2019, China’s arable land transfer area has reached 440 million mu, accounting for 30.8% of the total contracted arable land area. With the subsequent land transfer entering a standardized and normalized stage, the scale operation of agricultural production is bound to speed up. In recent years, due to the application of big data technology in agricultural production, China’s modern agricultural industry has developed rapidly, with the mechanization rate of farming at around 86% and the contribution rate of science and technology at over 67%, and agricultural production has gained breakthroughs nationwide. Thus, it can be seen that the modern agricultural industry in the context of big data will usher in new development opportunities.