Abstract: Complex intersection waters, characterized by heavy ship traffic and frequent route crossings, exhibit complex traffic dynamics and a high risk of collisions, making route optimization in such areas highly important. Traditional route optimization methods tend to focus more on the formulation of traffic rules and traffic control measures. While effective, these approaches often rely on the subjective experience of maritime managers and lack an objective basis. To address these limitations, this paper proposes a route optimization method based on historical ship trajectory data. The ship traffic network is extracted through trajectory clustering and image processing techniques. A node similarity model is constructed, and a clustering algorithm is applied to partition the overall network into multiple local traffic networks. Route optimization is then achieved by merging nodes and reconstructing the network within each community. Experimental results demonstrate that the proposed method reduces the complexity of ship traffic by 50% and the risk of ship collisions by 30% compared to preoptimized conditions. These improvements significantly enhance navigation safety, alleviate the regulatory burden on maritime authorities, and provide valuable insights for the planning of ship routing systems.