Intelligent education research has become a research hotspot in recent years. The Citespace software that operates a graph visualization function was used to clarify the current situation, hot spots, and evolutionary trends of intelligent education research development; the authors, institutions, and countries engaged in intelligent education research, as well as the basic knowledge structure, main keywords, citation clustering, dual-map overlay of journals and citation emergence of intelligent education research. The results show that the annual number of publications in the field has shown an upward trend since 2010, with strong communication among research institutions and countries, but weak communication among researchers. Among them, the United States is the center of the global collaborative network of intelligent education research. The basic knowledge structure of intelligence education research is mainly composed of Classroom Management, Evaluation Index, 5G Network, and Big Data Analytics. The dual-map overlay analysis of journals shows that the core areas of intelligence education are increasing, and the analysis of keywords and cited literature shows that Intelligence Tutoring System, AI system, Students and Education, Model, and System are high-frequency words with high-intensity burstness. In addition, research on intelligent education is characterized by multi-country, multi-field, and multi-disciplinary integration, and the adoption of Big Data, Distance Education Technology and Artificial Intelligence Technology to provide scientific support for teaching and learning will become the key research content in this field in the future.

With the rapid development of emerging technologies such as big data, artificial intelligence, and blockchain and their wide application in education, digital education has received widespread attention in the international education field. The outbreak of COVID-19 in December 2019 further catalyzed the digitalization process in various industries, including education, and forced the education system to carry out digital reform and innovation. Digital education transformation has become a new hotspot of great interest in countries around the world and a major direction for education reform practices. Therefore, to better understand the status of global digital education research, this study uses CiteSpace (6.1.R2) visual analysis software to visualize and quantitatively analyze the literature on digital education research in the social science citation index (SSCI). First, the basic information of digital education was analyzed in terms of annual publication volume, authors, countries, and research institutions. Secondly, the main fields, basic contents, and research hotspots of digital education research were analyzed by keyword co-occurrence analysis mapping and keyword time zone mapping. Finally, the research frontiers and development trends of digital education between 2000 and 6 September 2022 were analyzed by cocitation clustering and citations. The results show that, based on the changes in annual publication volume, we can divide the development pulse of the digital education research field into three stages: the budding stage (2000–2006), the slow development stage (2007–2017), and the rapid development stage (6 September 2018–2022); there are 26 core authors in this field of research, among which Selwyn N has the highest number of publications; the USA, England, Spain, Australia, and Germany have the highest number of publications; Open Univ is the institution with the most publications; digital education’s research hotspots are mainly focused on interdisciplinary field practice research and adaptive education research based on big data support. The research frontiers are mainly related to five areas: interdisciplinary development, educational equity, digital education practice, digital education evaluation, and digital education governance. This paper systematically analyzes the latest developments in global digital education research, and objectively predicts that human–computer interdisciplinary teaching models and smart education may become a future development trend of digital education. The findings of this study are useful to readers for understanding the full picture of digital education research so that researchers can conduct more in-depth and targeted research to promote better development of digital education.

The goal of education for sustainable development is to prepare future citizens to make informed decisions and take responsible action to solve problems. The purpose of mathematical literacy is to ensure that all learners develop an understanding of mathematics, and how to relate mathematics to the world and use mathematical knowledge to make valuable decisions in their lives, work, and society. It can be seen that the purpose of mathematical literacy coincides with the goal of education for sustainable development. In addition, math literacy is closely related to self-regulated learning (SRL), which is the key to meaningful learning and sustainable development. In educational research, it is an essential task to cultivate learners’ mathematical literacy and promote their sustainable development. With the rapid growth of emerging technologies, the emergence of big data has brought numerous challenges to various research fields. In the age of big data, educational research that can identify research perspectives and hotspots and summarize research evolution rules from a large body of literature can assist us in deepening subsequent analysis. As a result, in this study, we used CiteSpace and HistCite knowledge map visualization and exploration technology to examine mathematical literacy research trends, major research countries and regions, major research institutions, significant researchers, highly cited papers, research hotspots, and evolution trends on a global scale. Through this study, we found that the earliest literature on mathematical literacy appeared in 1957, and the research on mathematical literacy can be divided into three germination stages (1957–2001), a slow development stage (2001–2011), and a prosperous development stage (2011–2022). Most studies come from developed countries such as the US, the UK, Germany, and Australia. The Universities of Utrecht and Purdue University were the most published institutions, and scholars at Purpura published the most articles. The research object of highly cited literature is mainly children, and the research is primarily carried out through the measurement of students’ mathematical ability and achievement and the analysis of related influencing factors, which provides a direction for how to improve students’ mathematical literacy. The research on mathematical literacy mainly includes four research hotspots: working memory and mathematical literacy; brain science and mathematical literacy; mathematical achievement and mathematical literacy; and the generation strategy of mathematical literacy. The research field of mathematics literacy mainly includes working memory, parietal cortex, math performance, mathematics education, early childhood, parental belief, fractions, cognitive development, and student learning. There are 10 clusters. Different clusters have different evolutionary trends. With the evolution of time, working memory, mathematical education, fractions, and precinct beliefs clustered, gradually expanding from the concentrated research direction to the subdivision field. The clusters of parietal cortex, math performance, early childhood, cognitive development, and students do not show large keyword nodes during the research period. With time, it has gradually expanded from the centralized research direction to the subdivision field. The parietal cortex, math performance, early childhood, cognitive development, and students clusters did not show large keyword nodes during the whole study period.

Self-regulated learning (SRL) has been an important topic in the field of global educational psychology research since the last century, and its emergence is related to researchers’ reflections on several educational reforms. To better study the research history and developmental trend of SRL, in this work, the Web of Science core collection database was used as a sample source, “self-regulated learning” was searched as the theme, and 1218 SSCI documents were collected from 30 September 1986, to 2022. We used CiteSpace software to visualize and analyze the number of publications, countries, institutions, researchers, keywords, highly cited literature, authors’ co-citations, keyword clustering, and timeline in the field of self-regulated learning research, and to draw related maps. It was found that the articles related to self-regulated learning were first published in the American Journal of Educational Research in 1986, and that self-regulated learning-related research has received increasing attention in recent decades, wherein research on self-regulated learning is roughly divided into three periods: the budding period from 1986 to 2002, the flat development period from 2003 to 2009, and the rapid development period from 2010 to 2022. The number of papers published in the United States, China, Australia, and Germany is relatively high, and the number of papers published in Spain is low compared with that in the United States. During this period, the University of North Carolina in the United States and McGill University in Canada were the institutions with the most publications; Azevedo Roger and Lajoie Susanne P were the most-published scholars in the field of self-regulated learning research; the journal publication with the highest impact factor was Computers Education; and the primary research interests in self-regulated learning mainly focused on Performance, Strategy, Students, Achievement, Motivation, and Metacognition. Furthermore, the most-cited study related to SRL research was Formative assessment and self-regulated learning: a model and seven principles of good feedback practice.

With the rapid development of the global digital knowledge economy, educational activities are facing more challenges. Sustainable development education aims to cultivate students’ thinking ability to better integrate with the contemporary world view, so classroom practice should involve innovative teaching and learning. The goal of sustainable development education is to cultivate talents with high-level thinking and sustainable development abilities. The concept of deep learning emphasizes mobilizing students’ internal motivation, focusing on problem-solving ability, improving students’ critical thinking level, and developing students’ lifelong learning ability. The concept of deep learning has evolved with the times. The introduction of the concept of deep learning in teaching can enhance students’ understanding of the nature of knowledge, cultivate students’ high-level thinking, and enable students to achieve better learning results. Integrating the concept of deep learning into teaching has extremely important significance and value for sustainable development education. It has become a hot topic in the world to comprehensively analyze the research status of deep learning and explore how deep learning can help education achieve sustainable development. In this study, CiteSpace (6.1.R2) visualization analysis software was used to visualize and quantitatively analyze the literature on deep learning in the Social Science Citation Index (SSCI). The visualized analysis is conducted on the annual publication amount, authors, institutions, countries, keywords, and high-frequency cited words of deep learning, to obtain the basic information, development status, hot spots, and evolution trends of deep learning research. The results show that the annual publication volume of deep learning is on the rise; deep learning research has entered a rapid growth stage since 2007; the United States has published the most papers and is the center of the global deep learning research collaboration network; the countries involved in the study were often interconnected, but the institutions and authors were relatively dispersed; research in the field of deep learning mainly focuses on concept exploration, influencing factors, implementation strategies and effectiveness of deep learning; learning method, learning strategy, curriculum design, interactive learning environment are the high-frequency keywords of deep learning research. It can be seen that deep learning research has the characteristics of transnationality, multidisciplinary nature and multi-perspective. In addition, this paper systematically analyzes the latest progress in global deep learning research and objectively predicts that using intelligent technology to design appropriate teaching and learning scenarios and evaluation methods may become the future development trend of deep learning. The research results of this paper will help readers to have a comprehensive understanding of deep learning research, provide deeper and more targeted resources for integrating deep learning concepts into teaching, and promote better sustainable development of education.