With the rapid growth of the low-altitude economy, path planning for Unmanned Aerial Vehicles (UAVs) in complex urban low-altitude environments has become increasingly critical. However, urban low-altitude scenarios are influenced by buildings, meteorological conditions, regulatory restrictions and numerous factors. Traditional path planning methods struggle to effectively consider the impact of multiple constraints, making it challenging to provide effective and interpretable decision support for flight operations. This study proposes an optimized UAV flight path planning method based on an Urban Low-Altitude Navigation Knowledge Graph (ULAN-KG). Utilizing the knowledge graph, it structures the association between low-altitude flight route elements and low-altitude flight constraint factors in the urban space. The experiment selects a densely built area of Beijing for validation, where the proposed method is compared with traditional algorithms. The experimental results show that the A* algorithm improved by ULAN-KG can effectively avoid flight segments affected by strong wind conditions. When conflicting with controlled airspace events, the path planning results prioritize avoiding no-fly zones. This approach offers efficient and reliable technical support for UAV applications in complex urban low-altitude scenarios, such as logistics and emergency response.