In order to respond to the national low carbon economy policy and achieve sustainable development, logistics enterprises must reduce carbon (Low Carbon, LC) emissions and improve energy use efficiency through technological innovation and management optimization. In the study, we first collected operational data from several typical logistics enterprises, including energy consumption, carbon emission, transportation efficiency and other indicators. Then, by cleaning and preprocessing these data, a regression model based on the Adaboost algorithm was constructed with the aim of assessing the LC transformation effectiveness of each enterprise. The Adaboost model exhibits low MSE (Mean Square Error) at all training data ratios, especially at higher data volume (0.9), the MSE is further reduced to 0.316. In summary, this paper deeply analyzes the effect of LC transformation of logistics enterprises and its influencing factors by applying the Adaboost regression algorithm, in order to provide a new path for logistics enterprises’ low-carbon development to open up new paths.