Limited by ecological conditions, in order to improve the innovation efficiency concerning energy, a change from “extensive” to “green” is required. The development of sustainable technology is the most important productive force. Economic development is centered on finance. Only by mutual penetration and interaction can China’s energy innovation be effectively promoted. “Green GDP (Gross Domestic Product)” is the main direction of China’s current economic development. A sustainable green digital economy is the primary condition for promoting energy efficiency, and it is the key to improving energy efficiency in China to achieve a green transition. Green finance for sustainable development refers to economic finance centered on energy conservation, environmental protection and technological innovation. Attention is constantly paid to the financial industry in China, and we are trying to combine the concept of environmental protection with financial development to promote the continuous development of China’s environmental protection economy. A company’s economic and environmental benefits will be improved from the point of view of improving resource conservation and reducing the environmental impact of equipment and processes. To achieve this goal, the “efficiency effect” of the financial system needs to be fully exploited to maximize energy efficiency. An empirical study about the relationship of financial performance and energy efficiency in the green sector is carried out while considering the environmental constraints. The results show that the expansion of the financial scale and the adjustment of the financial structure have increased the energy utilization efficiency by more than 15% from the past to the present and reduced the pollution of the ecological environment by more than 10% nationwide, realizing the effective allocation of resources. Digital finance is a new generation of financial services that combines the Internet and information technology with traditional financial service formats. Including Internet payment, mobile payment, online banking, financial service outsourcing and online loans, online insurance, online funds and other financial services. Therefore, the sustainable development of all aspects of finance has a huge catalytic effect on the doubling of energy utilization efficiency.

China is a big agricultural country, and Chinas agricultural development is related to the development of the country. Especially in poor rural areas, the development of agriculture still stays at low-level manual farming. Therefore, with the help of the developed Internet of Things technology, it is necessary for digital technology to develop and reform the rural areas in poverty-stricken areas. This paper aims to promote the supply-side structural revolution of agriculture in poverty-stricken areas by digital technology based on intelligent environment of Internet of Things. Firstly, the KNN intelligent algorithm is improved to improve the accuracy and reduce the time complexity. Then, based on the data support of Internet of Things technology, a prediction model is built, and the parameters are optimized by particle swarm optimization, and finally the best prediction model with high prediction accuracy, good stability and reduced prediction time complexity is obtained. Taking the source area of the Danjiang River in the South-to-North Water Transfer Project as the research object, the total grain output and soybean production demand in this area from 2010 to 2018 were selected, and the change trend of soybean production and demand in this area from 2019 to 2021 was predicted. The experimental data show that the soybean output in this region has increased year by year, with a cumulative increase of 6,985.4 tons from 2010 to 2018. Soybean output sometimes exceeds demand, and sometimes it is less than demand. There will be an oversupply situation in 2019 and 2021, and an oversupply situation in 2020. Therefore, the prediction model of Internet of Things can effectively predict the supply and demand situation of soybean with an accuracy rate of over 90, which can provide data support for the decision-making of structural change of agricultural supply side in poverty-stricken areas.