Abstract: In the process of powder compaction, the die wear analysis plays an important role in ensuring the quality and precision of products. Based on the Archard wear model, BP neural network and finite element simulation, the influences of process parameters on the wear of iron based cycloidal rotor were studied. Firstly, the mapping relationship among Poisson’s ratio, elastic modulus and density of iron base material was obtained by powder compaction test. The friction coefficient,initial hardness, lowering speed and pressing mode were selected as technological parameters to construct the orthogonal tests with three factors and four levels and one factor and two levels. Secondly, the numerical simulation of the pressing process was realized based on the finite element software DEFORM-3D. Four different process parameters were taken as the input and the wear of the die as the output, and a three-layer BP neural network model of 4 ×13 ×1 was built. Finally, the genetic algorithm was combined to complete the iterative optimization in the range of parameter values to obtain the best combination of technological parameters. The results show that the average relative error between the predicted value and the actual value of the neural network is only 4.45%. The optimal process parameters are as follows: friction coefficient 0.125, initial hardness63 HRC, press speed 1.558 mm/s. The pressing method is two-way pressing, and the minimum wear quantity is 1.5521 ×10-5mm, which can improve the service life of the die.