Agricultural mechanization service (AMS) is a critical path to achieving agricultural green transformation with smallholders as the mainstay of agricultural production. Based on the panel data of 30 Chinese provinces from 2011 to 2020, this paper measures the AGTFP using the Super-SBM model and examines the effects of different AMS supply agents on AGTFP and spatial spillover effects through the spatial Durbin model. The main conclusions are as follows: First, China’s AGTFP showed a stable growth trend, with the mean value increasing from 0.1990 in 2011 to 0.5590 in 2020. Second, the specialization (SPO) and large-scale (LSO) of AMS supply organizations have significantly positive effect on the AGTFP of the local province. However, SPO has a significantly positive effect on the AGTFP of the neighboring provinces, while LSO has the opposite effect. Third, the specialization of AMS supply individuals (SPI) has significantly negative effect on the AGTFP of the local province. In contrast, the large-scale AMS supply individuals (LSI) has the opposite effect. Furthermore, the spatial spillover effects of both are insignificant. Fourth, the spatial spillover effect of AGTFP shows asymmetry among different regions and indicates that AMS resources flow from non-main grain production and economically developed regions to main grain production and less developed regions. These findings provide helpful policy references for constructing and improving the agricultural mechanization service system and realizing the agricultural green transformation in economies as the mainstay of agricultural production.

The Internet of Things economy is necessary for the reform of China’s economic industrial structure and international development, and it brings new opportunities and new challenges to the Chinese economy. The comprehensive informatization of the society is the prerequisite for the economic development of the Internet of Things. The Chinese government will adopt a strategy of simultaneous development to continue to accelerate the development of traditional agriculture while developing emerging strategic agriculture of the Internet of Things, accelerating the transformation of economic production methods. Family farming is a new type of agricultural business in the microeconomy. It is not only a producer and mobilizer of agricultural products but also an important carrier for implementing agricultural innovation technologies and promoting agricultural modernization. This article uses game theory, resource endowment theory, expectation theory, etc. to describe the impact mechanism of economies of scale, profit maximization, and environmental behavior on family farms from a game perspective and discusses the specific effects of resource endowments and psychological expectations. Through the research of the Internet of Things and the rural green energy cycle, this article applies it to the new agricultural business entities, thereby promoting the development of proenvironmental behavior analysis.

During the COVID-19 pandemic, online learning has become one of the important ways of higher education because it is not confined by time and place. How to ensure the effectiveness of online learning has become the focus of education research, and the role of the “online learning community” cannot be ignored. In the context of the Internet of Things (IoT), we try to build up a new online learning community model: (1) First, we introduce the Kolb learning style theory to identify different online learning styles; (2) Second, we use a clustering algorithm to identify the nature of different learning style groups; and (3) Third, we introduce the group dynamics theory to design the dimensions of the questionnaire and combine the Analytic Hierarchy Process (AHP) method to identify the key influencing factors of the online learning community. We take business administration majors and students in universities as an example. The results show that (1) as a machine learning method, the clustering algorithm method is superior to the random construction method in identifying different learning style groups, and (2) our method can well judge the importance of each factor based on hierarchical analysis and clarify the different roles of factors in the process of knowledge transfer. This study can provide a useful reference for the sustainable development of online learning in higher education.