Abstract: High-standard farmland construction is an important foundation for improving agricultural production conditions and promoting digital agriculture. By improving land conditions and agricultural infrastructure, it creates favorable conditions for the application of precision and intelligent agricultural production technologies. Based on survey data from 949 farming households in Jiangxi Province, this study employs an ordered Probit model and a mediation effect model to examine the impact of high-standard farmland construction on farmers’ adoption of digital agricultural production technologies and its underlying mechanisms. The results show that: 1) high-standard farmland construction significantly promotes farmers’ adoption of digital agricultural production technologies, and the conclusion remains robust after replacing the core explanatory variable, dependent variable, and estimation model; 2) mechanism analysis shows that scale operation and agricultural mechanization services are important pathways through which high-standard farmland construction promotes farmers’ adoption of digital agricultural production technologies. High-standard farmland construction promotes technology adoption by expanding the scale of cultivated land operation and plot size and improving access to agricultural mechanization services; 3) heterogeneity analysis shows that the promoting effect of high-standard farmland construction on the adoption of digital agricultural production technologies is more pronounced among large-scale farmers, higher-income households, and farmers with less fragmented land. Therefore, efforts should be continued to promote high-standard farmland construction and improve post-construction management and maintenance mechanisms. Policy guidance should be strengthened to promote the coordinated development of high-standard farmland construction, scale operation, and agricultural mechanization services. Furthermore, promotion strategies for digital agricultural production technologies should be tailored to the production and operation characteristics of different types of farmers, thereby creating favorable conditions for their adoption of digital agricultural production technologies.