Recent innovations are increasingly recognizing applications in precision agricultural systems that use data science techniques as well as so-called machine learning techniques. Big data analytics have created various data-intensive decision-making opportunities. This study reviews the big data analysis practices in the agriculture industry to resolve various problems to provide prospects and exciting fields of application in China. In the successful implementation of precise farming, the high-volume and complicated data generated present challenges for the economic growth of China. Emerging deep learning techniques seem promising and must be reinvented to meet current challenges. Thus, this paper suggests a big data analytics agriculture monitoring system (BDA-AMS) to ensure the highly accurate prediction of crop yield in precision agriculture and economic management using a deep learning algorithm. The convolution neural network gathers the raw images from UAVs and performs early predictions of crop yield. The simulation analysis using an open-source agricultural dataset resulted in a high parameter–precision ratio (98.8%), high accuracy (98.9%), a better performance ratio (95.5%), an improved data transmission rate (97.8%), a reduced power consumption ratio (18.8%), and an enhanced weather forecasting ratio (94.8%), production density ratio (98.8%), and reliability ratio (98.6%) compared to the baseline models.

Overeducation means that the rapid growth in the numbers of secondary and higher education graduates begins to exceed the actual demand of the labor market due to this excessive expansion of education. Consequently, educated workers are faced with knowledge unemployment, or are engaged in jobs that do not match their academic qualifications, resulting in a decline in income and a waste of educational resources. In order to explore the effect of overeducation on workers’ job satisfaction, we selected data from China Household Tracking Survey (CFPS) and conducted a fixed-effect ordered logit model regression analysis. It was found that overeducation has a negative impact on employees’ job satisfaction and an impact on wage penalty. Wage income has a mediating effect on the relationship between overeducation and job satisfaction. We present three policy suggestions: for the Government’s administration department, it is necessary to actively create an environment for matching education and occupation; to improve the possibility of matching education and occupation; and to reduce the negative effect of labor contracts on the improvement of human capital and the job satisfaction of overeducators by adjusting the flexibility and stability of the labor contract. For institutions of higher learning: it is necessary to make forward-looking adjustments to the educational structure, according to the actual needs of economic and social development to adapt to the social demand for talent and development trends; to train highly skilled and high-quality workers needed for social development; and to reduce the unreasonable distribution of resources caused by overeducation. For enterprises: employees should be guided to correctly understand the unpredictable relationship between education and work and reasonably reduce the job expectations of new employees, according to their own work experience and technical level.

In this paper, we examine China’s policy environment from the perspective of policy sustainability. Through our use of the CiteSpace and NVivo software, the literature on the policy environment is visualized using a knowledge graph analysis and a content analysis of highly cited papers. The analysis shows that: the number of published papers on “policy environment” is on the rise, and the research prospect is broad. There is a lack of communication and cooperation between domestic authors and a close relationship between international authors. The main research countries included the USA, Switzerland, Finland, Ireland, and Austria, etc. Domestic research focuses on vehicles for high-quality economic and social development, while international research focuses on theoretical model discussion. The domestic optimization path focuses on the transformation from “Sending blood” (“ShuXieShi”) to “Making blood” (“ZaoXieShi”), while the international research focuses on the main line of “public health and environmental protection”. Domestic research focuses on fostering a sound business environment, while international research focuses on the public, political and professional aspects of health and medical policy environment. On the whole, in the future, we should improve the adaptability of the policy environment, optimize the policy implementation environment, and establish the interaction mechanism of policy environment optimization to achieve policy optimization.

Focusing on the difficulties presented by conceptual metaphors in Big Data (BD)-related literature, this research offers a novel methodology for improving English translation processing. The main goal of this study is to improve translation efficiency and accuracy via cutting-edge technologies, including machine learning, cloud computing, and big data analytics. English texts with complex metaphors are gathered and annotated for the study, and then the suggested model is compared to more conventional translation techniques. The results show that the optimized translation model performs better than traditional methods. Evaluation measures, particularly the Translation Edit Rate (TER) and Bilingual Evaluation Understudy (BLEU), show that the model records lower TER scores, which indicate fewer changes required for correctness, and higher BLEU scores, which indicate enhanced translation quality. The optimized model’s performance stabilizes as the amount of text rises, demonstrating how resilient it is to processing bigger datasets. This study shows how well the suggested model enhances translation results and illustrates how crucial it is to comprehend metaphorical language in technical situations. This research aims to give translators a better foundation for handling the difficulties of writing about Big Data by tackling the nuances of interpreting conceptual metaphors. Finally, given the quickly changing world of technology, the knowledge gathered from this study advances translation techniques and improves interlingual communication.