This paper presents a detailed analysis of cloud-to-ground (CG) lightning data collected by the Shaanxi VLF/LF lightning location system over a period of seven years, from 2017 to 2023. The research begins by constructing a CG lightning kernel density surface, which is derived from the collected data and implemented using intensity weighting through a Geographic Information System (GIS) kernel density field model. This model facilitates the visualisation and quantification of lightning activity across the Shaanxi region, thus providing a robust foundation for subsequent analyses. Upon establishing the kernel density surface, the study utilises a hotspot detection model to identify and categorise regions with high lightning density. This crucial step pinpoints specific areas where lightning activity is exceptionally intense, thereby offering vital data for risk assessment and mitigation strategies. The analysis explores the relationships between the distribution of CG lightning kernel density, its identified hotspots, and various environmental factors such as topography and vegetation coverage, both of which significantly influence lightning activity. Key insights revealed by the findings include the effectiveness of the GIS kernel density field model and the hotspot detection model in accurately identifying regions with intense lightning activity. This enhanced understanding of the spatial distribution characteristics of CG lightning is essential for predicting and managing lightning-related hazards. Additionally, the research identifies significant spatiotemporal variations in CG lightning activity within the Shaanxi region. The analysis shows that lightning activity is most frequent and intense during the summer months, followed by spring and autumn, with winter experiencing the least activity. Spatially, the lightning density is higher in the northern and southern parts of Shaanxi, while the central region shows relatively lower density. Further, the study examines the correlation between kernel density and altitude. A positive correlation is observed below 1 km in altitude, suggesting that lightning activity increases with altitude. Above 1 km, however, this trend reverses, with a decrease in kernel density as altitude increases. The research also investigates how topography and vegetation impact kernel density values, revealing that both factors significantly affect kernel density, with topography having a more pronounced impact. This indicates that variations in terrain play a crucial role in shaping the patterns of lightning activity. In conclusion, the study identifies the primary distribution areas for large and medium-large hotspots, predominantly located in grasslands and forest areas within mid-altitude loess ridges and mounds, as well as moderately undulating mountains. These findings are instrumental for targeting lightning protection measures and enhancing safety in these vulnerable areas. Overall, this paper significantly advances understanding of lightning activity in the Shaanxi region, providing valuable insights for future research and practical applications, thereby enriching the scientific community’s resources for better managing the risks associated with lightning.