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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.