Objective To estimate knee adduction moment (KAM) and knee flexion moment (KFM) under different gait test conditions via an inertial sensor network (ISN). Methods Twelve healthy young male subjects wore eight inertial sensors (located in the trunk, pelvis, both thighs, both shanks, both feet) and walked under different test conditions (changing foot progression angle, trunk sway angle, step width and walking speed). An ISN was used to extract biomechanical features as the input of recurrent neural network (RNN), so as to estimate the KAM and KFM. Results The overall KAM estimation accuracy: relative root mean square error (rRMSE) was 8.54% and r=0.84. The overall KFM estimation accuracy was rRMSE=6.40% and r=0.94. Conclusions The model can be used as the basis for load estimation of knee joints out of the lab and its potential application includes gait training and rehabilitation assessment after knee surgery.