Sustainable agriculture is crucial to the United Nations’ efforts to promote the Sustainable Development Goals (SDGs). However, to develop successful policies and strategies, it is necessary to assess the many obstacles to implementing sustainable agriculture. This study uses Multi-Criteria Decision Making (MCDM) techniques to analyze the challenges and opportunities facing sustainable agriculture in China’s economy, particularly in advancing the SDGs. Three enormous obstacles are found in the study, along with fifteen smaller ones that are broken down into economic, social, and environmental categories. The weights of the obstacles and sub-barriers are determined, and the solutions for sustainable agriculture are ranked using the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methodologies. According to the AHP approach, economic issues are China’s most significant obstacles to sustainable agriculture, followed by environmental and social concerns. Climate change and a lack of financial incentives are the two highest-ranked sub-barriers. On the other hand, the SAW approach suggests that the best ways to achieve the SDGs through sustainable agriculture are through international cooperation, financial investments in sustainable agriculture, and alternative agricultural methods. In order to advance sustainable agriculture and the SDGs in China, the report advises policymakers to focus on strengthening institutional support, increasing public awareness, and making technological investments.

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The question of how to proactively respond to population aging has become a major global issue. As a country with the largest elderly population in the world, China suffers a stronger shock from population aging, which makes it more urgent to transform its industrial and economic development model. Concretely, in the context of the new macroeconomic environment that has undergone profound changes, the shock of population aging makes the traditional industrial structure upgrading model (driven by large-scale factor inputs, imitation innovation and low-cost technological progress, and strong external demand) more unsustainable, and China has an urgent need to transform it to a more sustainable one. Only with an in-depth analysis of the influence mechanism of population aging on the upgrading of industrial structure can we better promote industrial structure upgrading under the impact of population aging. Therefore, six MSVAR models were constructed from each environmental perspective based on data from 1987 to 2021. The probabilities of regime transition figures show that the influencing mechanisms have a clear two-regime feature from any view; specifically, the omnidirectional environmental transition occurs in 2019. A further impulse–response analysis shows that, comparatively speaking, under the new environment regime the acceleration of population aging (1) aggravates the labor shortage, thus narrowing the industrial structure upgrading ranges; (2) has a negative, rather than positive, impact on the capital stock, but leads to a cumulative increase in industrial structure upgrading; (3) forces weaker technological progress, but further leads to a stronger impact on the industrial structure upgrading; (4) forces greater consumption upgrading, which further weakens industrial structure upgrading; (5) narrows rather than expands the upgrading of investment and industrial structures; and (6) narrows the upgrading of export and industrial structures. Therefore, we should collaboratively promote industrial structure upgrading from the supply side relying heavily on independent innovation and talent, and the demand side relying heavily on the upgrading of domestic consumption and exports.

In order to improve the effect of bank interest rate volatility analysis, this article combines actual conditions and machine learning algorithms to construct a fluctuation analysis model of bank interest rate based on computer statistical model and machine learning. For the data with system transformation, the data contains stationary and nonstationary processes; so, the power of the standard unit root test is low. This paper therefore proposes a new unit root test method. From demand analysis, system design to system implementation, and testing, advanced software engineering-related ideas are adopted, and the bank’s interest rate management system is designed and implemented in strict accordance with software development-related processes. This paper adopts the modular design idea, classifies the functions to be realized according to their content, and conducts structural verification and performance analysis of the functional modules. Through experimental analysis, we can see that the system model constructed in this paper has certain effects in the analysis of interest rate fluctuations.