Abstract: A Vector Auto Regression（VAR） model is constructed by combining the China Containerized Freight Index of E/C America Service（CCFI E/C America Service） and the China Containerized Freight Index of W/C America Service（CCFI W/C America Service） with the Clarksons Container ship Port Congestion Index（CPCI） from January 2018 to February 2023, to quantitatively analyze the impact mechanism of port congestion on container freight rates. The model also incorporates a Vector Error Correction（VEC） model to study the long-run equilibrium relationship between the variables. The results show that: 1) Port congestion leads to the occupation of container capacity and port resources, as well as changes in the distribution of capacity and transportation strategies on the China-U.S. export container routes, which in turn causes different degrees of fluctuations in CCFI on the sub-routes; 2) The effect of port congestion on container freight rates persists for nearly three months; 3) Regardless of the U.S. East route or the U.S. West route, port congestion in the U.S. has a more significant impact on promoting the increase of the container freight index compared to port congestion in China. Meanwhile, this paper provides a new perspective for predicting CCFI by investigating the impact mechanism of port congestion on CCFI fluctuations.