In the logistics and trade that are highly dependent on containers, efficient identification of col-lided positions is of great significance for enhancing cargo safety supervision and accident respon-sibility. Traditional methods that rely on visual inspections require a lot of manpower, are time-consuming and costly. This study proposes a machine learning-based system to identify col-lided positions using the data collected through accelerometers installed on container doors. This study also uses feature selection techniques to reduce the data dimensionality, thereby improving computational efficiency and reducing computational costs. The feature selection methods evalu-ated include: Pearson Correlation Coefficient, Mutual Information, Sequential Forward Selection, Sequential Backward Selection, and Extreme Tree. The machine learning models evaluated in-clude Decision Tree, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting. Experimental results show that feature selection effectively reduces data di-mensionality and computational cost while maintaining or improving classification accuracy. The best combination of classification model and feature selection method is the combination of K-Nearest Neighbor and Extreme Tree, which achieved the best balance in terms of accuracy 0.9712, execution time 0.0300 seconds and CPU utilization 0.10%. It has a considerably practical significance in the logistics field in terms of safer and more efficient cargo transportation, reliable accident responsibility identification with reduced computing costs.