The rapid evolution of precision agriculture and intelligent farming systems has significantly transformed global agricultural production paradigms, offering data-driven, efficient, and sustainable solutions to traditional rural development challenges. This study proposes an intelligent systems-based evaluation model for assessing rural agricultural development in China, drawing insights from international precision farming technologies. The research integrates big data analytics, machine learning, IoT-enabled agricultural systems, and policy-driven agricultural modernization frameworks to construct a multi-layered evaluation architecture. Building on prior advancements in smart farming and agricultural digitalization (Alfred, 2021), the study synthesizes global practices and adapts them to the Chinese rural agricultural context, emphasizing productivity, sustainability, and technological adoption. The methodology employs a hybrid analytical framework combining indicator-based evaluation, system dynamics modeling, and intelligent decision-support mechanisms. Findings suggest that precision agriculture technologies significantly enhance resource efficiency, yield optimization, and environmental sustainability, while also revealing gaps in technological accessibility and regional implementation disparities. The study contributes to the theoretical advancement of intelligent agricultural evaluation systems and provides actionable insights for policymakers and agricultural planners aiming to modernize rural development systems in China.