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An increasing number of research literature studies about green technology have been accepted by journals of different disciplines due to the rapid technical progress and innovation in all types of industry. This study uses bibliometric tools of CiteSpace and VOSviewer to analyse the key authors’ co-citation network, institution cooperation, the keyword clusters of green technology, and the evolution trend of green technology. We find that since 1960s, the number of research papers with green technology theme has been growing. These papers are mainly involved in fields of economics and business, engineering, and chemistry, in which there exist 13 highest cited papers from 2009 to 2020 based on the Web of Science database. In this study, we find the top 20 journals of green technology with the parameter of literature count and centrality. We find that the cooperation of authors is quite weak with the co-authorship analysis, and we obtain the top 12 institutions in 15 countries dominantly in green technology research through country and institution analysis. We conduct a cluster analysis of keywords related to green technology, and we obtain 10 clusters, with three economical clusters, five engineering clusters, and two chemical clusters. Finally, we summarise green technology with the aid of the timeline view function of CiteSpace system.

The traditional data envelopment analysis (DEA) method used for performance evaluation has inherent problems such as being easily affected by statistical noise in data. Furthermore, when new evaluation units are added, the performance of all the original units must be re-measured, which restricts the evaluation efficiency. In this study, machine learning algorithms were applied to make up for the shortcomings of the data envelopment analysis method. First, a super-efficiency SBM model was used to construct the relative effective frontier, and then machine learning algorithms were used to construct a regression model and establish the absolute effective frontier. After 15 machine learning algorithms were compared, BPNN demonstrated the best performance, and a SuperSBM-DEA-BPNN model was eventually established. The new model has the following advantages: First, compared with the traditional data envelopment analysis method, the absolute effective frontier displays better evaluation; second, compared with the data envelopment analysis and neural network fusion outlined in the previous literature, the new model can better overcome the problems associated with data envelopment analysis, thereby improving the fusion efficiency. Taking the innovation efficiency evaluation of China's regional rural commercial banks for instance, the new model is proven to be more applicable and offers more effective management tools to improve efficiency. On the whole, the new model not only provides a stable performance evaluation tool but also facilitates comparison, which has good application significance for organizations.