In order to analyze and predict the fatigue life of the shearer’s cutting planet carrier,the shearer’s rigid-flexible coupling model was established,the planet carrier’s dynamic characteristics was studied,and the planet carrier’s maximum stress value was obtained as the input. The planet carrier’s fatigue life was obtained by using n Soft,and the planet carrier’s fatigue life,under different working conditions was predicted by using BP,PSO-BP and improved PSO-BP. The results show that under the condition of solidity coefficient of 8. 4,depth of cut-off 600 mm,rotational speed of 90 r/min and traction speed of 2. 5 m/min,the maximum stress is 554. 78 MPa,the planet carrier’s stress concentration area is at the spline receding groove,and the fatigue life is 5. 507 2×106. Among the three neural network models,BP,PSO-BP and the improved PSO-BP,the predicted fatigue life’s maximum relative error is 4. 35%,2. 52% and 0. 90%,and the iteration ’s number is 23,8 and 6 times,respectively. The improved PSO-BPNN model improves the prediction accuracy and also improves the iterative speed. Combining MATLAB、ANSYS、ADAMS、n Soft and improved PSO-BPNN,this paper provides a method for predicting the fatigue life of key parts of industrial and mining equipment.