With the escalating demand for renewable energy, photovoltaic (PV) power stations, as a vital source of clean energy, have seen the optimization of their performance and efficiency enhancement become increasingly crucial. However, conventional PV stations lack adaptability to their installation environments and often struggle to balance power generation efficiency with construction costs. Mistaken environmental assessments during the preliminary stages can lead to wasted investment or underutilization of solar resources. Consequently, this paper proposes an innovative adaptive PV power station system by integrating partial power converter (PPC) with artificial intelligence (AI) control strategies. The objective is to enhance PV power station generation efficiency, reduce system setup costs, and optimize energy allocation through the synergy of intelligent algorithms and PPC. The study delves into PPC, encompassing circuit configurations, theoretical analyses, waveforms during operation, and current flow patterns within a cycle. Additionally, it meticulously examines AI-based control methodologies. Findings reveal that employing PPC in lieu of full power converter (FPC) can significantly improve conversion efficiency. Leveraging AI control strategies, an environmental variable model is constructed based on collected data, including solar irradiance, temperature, humidity at the station site, and the output voltage and current, thereby augmenting the system’s capability to handle vast amounts of station data and fostering environment adaptability. Finally, an experimental system was constructed to validate these propositions. The empirical results endorse the proposed PV power station system’s characteristics: excellent environmental adaptability, high conversion efficiency, and low deployment costs, thereby furnishing a foundation for practical applications.