To ease the fluctuation of hog prices and maintain the hog market’s stability, the central government of China has issued a series of hog price control policies. This paper, supplemented by co-word analysis and LDA thematic modeling, constructed 9 first-level indicators and 36 second-level indicators and used a PMC index model to conduct quantitative research on the selected 74 policies and regulations of China’s pig price regulation policies from July 2007 to April 2020. The research concludes that the research tool system of China’s hog price control is formed. The overall design of the hog price control policy is relatively reasonable, but there are still the following problems: the subject of China’s pig price control policy is singular, so it is difficult to form a resultant force; the policy pays attention to the price regulation in the short term, but ignores the long-term industrial structure adjustment; it emphasizes market supervision, but insufficient support for slaughtering and processing; it focuses on production and management to improve the development quality and efficiency of the pig industry, but does not take social equity into account. Finally, some policy suggestions are put forward: multi-department division of labor and close cooperation; adjusting the industrial structure of hog and carrying out appropriate large-scale breeding; establishing the operation mode of slaughtering and processing in the producing area to reduce the circulation cost of the pig industry; ensuring the consumption of pork by low-income groups and giving consideration to social efficiency and equity.

(1) Background Genetic Modification (GM) refers to the transfer of genes with known functional traits into the target organism, and ultimately the acquisition of individuals with specific genetic traits. GM technology in China has developed rapidly. However, the process is controversial; thus, future development may be hindered. China has become the world’s largest importer of GM products. Research on the attitudes towards GM food in China will help the government achieve sustainable development by better understanding and applications of the technology. (2) Methods This research utilizes data from Sina Weibo (microblog), one of the biggest social network services (SNS) in China. By using the self-created Python crawler program, comments related to the genetically modified food in the People’s Daily account are analyzed. Sentiment classifications are analyzed via multivariate logistic regression. (3) Results Based on the factor analysis, theme type characteristics, the propagation characteristics, the body information characteristics, and the comment characteristics have different degrees of influence on the user’s emotional distribution. (4) Conclusion Practical implications and conclusions are provided based on the results at the end.

In order to enrich the research on the influence of weather factors on agricultural economy and provide practical decision-making references for the relevant market entities, this study took pointed pepper, loofah, Chinese chives and tomato as examples, using weekly wholesale prices and corresponding weather factors data from one of the main production areas in China based on the vector autoregressive (VAR) and the time-varying parameter vector autoregressive (TVP-VAR) models to explore the dynamic impacts of weather changes on vegetable price fluctuations. It was found that price fluctuations of specific vegetable varieties were affected by changes in specific weather factors. The shock intensity of weather factor changes in the same lag period on the current vegetable price fluctuations was time-varying, which could describe the historical dynamics of the impacts of weather changes on vegetable price fluctuations. The dissipation characteristics of the dynamic impacts of weather factors changes occurring at selected time points on vegetable price fluctuations had strong consistency, and the obtained rules could be used to analyze the life cycle and dynamic characteristics of extreme weather impacts on vegetable price fluctuations. In addition, vegetable price fluctuations were affected mostly by their own lag periods, and the shock intensity of vegetable price fluctuations with equal lag periods on their own current price fluctuations were relatively stable. The dynamic impacts of vegetable price fluctuations occurring at chosen time points on its own later fluctuations were highly consistent in the variation from the beginning of the strongest positive shock to the final dissipation.

This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early‐warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick‐Prescott (H‐P) filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H‐P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others.

Vegetable prices play an important role in people’s daily lives globally. However, there is very little research that has systematically analyzed and discussed the existing research. Therefore, in order to narrow this gap in the literature, there is a need for an extensive and macro-level study of vegetable price research. By sorting out a large volume of documents, This paper presents a visual representation of the vegetable price research literature in the Web of Science and CNKI databases; it includes three perspectives of the cooperative network, co-occurrence network and co-cited network, and draws scientific knowledge map, to acquire a good understanding of the current situation of vegetable price research, hot issues, clearly shows the focus of the research, summaries the fluctuation pattern of vegetable price, deeply analyses the influencing factors of vegetable price fluctuation, and find out the connection between related scholars and the correlation existing in each research content. The results show that scholars at home and abroad have conducted research from the perspectives of vegetable price influencing factors, forecasting and early warning, and insurance systems, but they are only limited to some regions, and some vegetable varieties, and the forecasting and early warning of vegetable prices are not accurate enough, the insurance system is not perfect, and a complete price influencing mechanism has not yet been proposed. The aim of this study is to make a significant contribution to the scientific community and industry practitioners by providing a comprehensive understanding of the latest status, popular keywords and topics, and emerging areas of vegetable price research. In addition, this study will be a useful and valuable reference and guide for researchers interested in this field.