The identification of key path chains serves as the foundation for optimizing traffic control strategies. Based on trajectory data from partial vehicles in a mixed traffic flow environment, this paper proposes a key path chain identification method that integrates maximum a posteriori estimation with multi-metric fusion. First, leveraging road network topology and connected vehicle trajectory data, a joint identification model for path set classification is constructed using maximum a posteriori estimation theory, enabling the recognition of key path chain sets between different node pairs in the network. On this basis, considering the effects of traffic congestion hysteresis, path divergence characteristics, and dynamic flow fluctuations, three metrics—path flow aggregation capacity, path discreteness, and operational impedance—are proposed. A nonlinear key path chain set criticality evaluation and ranking model is established by integrating these three metrics. Simulation verification is conducted using road network and traffic survey data from a specific area in Nanchang. The rationality of the identification results is tested using path traffic transfer intensity, and the sensitivity of the model under different penetration rates is analyzed, demonstrating the effectiveness of the proposed model.