Objective: Wearable inertial sensor (IMU) technology and Random forest (RF) algorithm were used to detect the fatigue level of long-distance backpack walking, and the feasibility and optimal IMU combination scheme were explored. Methods: Thirty healthy male college students were recruited to carry out long-distance backpack walking. Xsens MVN Link inertial motion capture system and Borg-RPE fatigue scale were used to collect kinematic data and subjective fatigue values of load-bearing walking, and fatigue was divided into 3 levels: no fatigue, moderate fatigue and severe fatigue. The original data were extracted, gait segmentation, data screening and feature extraction were carried out, and RF model was used for machine learning of sample features. Finally, the accuracy rate, confusion matrix and AUC value were calculated to evaluate the detection effect of different IMU combinations. Results: The accuracy of one right femur IMU was 82.55%, and the accuracy of five IMU combinations was 87.94%. In a combination of IMUs, at least one upper body IMU is included, and the left limb has more IMU than the right. The RF model had a higher level of fatigue detection for load-bearing walking, and the AUC values of level 3 fatigue were 0.99, 0.97 and 0.99, respectively, when four IMUs were used. Conclusions: IMU technology and RF algorithm have high accuracy and classification ability in the third-level fatigue detection task of walking with backpack load. In practical application, it is recommended to use 1-5 IMUs, and the combination of upper body IMU and lower limb IMU configuration scheme.