This research aims to establish a prediction method for the initial productivity of directional wells in offshore sandstone oil reservoirs. A total of 17 factors affecting the initial productivity were considered from aspects of the geology，engineering，and development. Given 2 700 sets of data from 45 directional wells in offshore sandstone oil reservoirs，the Spearman correlation coefficient，random forest，and recursive feature elimination algorithm were combined to rank the importance of these influencing factors. With the logic of reservoir engineering，the main controlling factors in the initial productivity were selected. On this basis，the initial productivity prediction model was constructed by the extreme gradient boosting（XGBoost）algorithm，and its loss function was improved by referring to the productivity formula to enhance its physical constraints of this data mining algorithm. The results show that the main controlling factors affecting initial productivity of directional wells in offshore sandstone oil reservoirs include the formation flow coefficient，porosity，variation coefficient of the formation flow coefficient between layers，vertical depth of an electric submersible pump（ESP），perforation thickness of reservoirs，borehole size，drawdown，frequency of ESP，and choke size. Furthermore，the average relative error of the XGBoost algorithm with physical constraints for predicting the initial productivity of five wells is 9.68%，and the average relative error of the XGBoost algorithm without physical constraints is 11.68%. Therefore，physical constraints can effectively improve the accuracy of the XGBoost algorithm in productivity prediction，and the proposed method can realize the accurate prediction of the initial productivity of directional wells in offshore sandstone oil reservoirs.