In order to improve the positioning accuracy of pedestrian dead reckoning ( PDR) for low-cost inertial measurement unit (IMU) arrays, this paper proposes for the first time the use of multi-layer perceptron (MLP) to achieve algorithms for low-cost IMU array data fusion. The measurement data of the IMU array ( including triaxial acceleration and triaxial angular velocity) and the measurement data of the high-precision IMU are obtained by synchronizing the motion of the self-designed IMU array and the highprecision IMU, and the measurement data of the high-precision IMU is used as a label. The MLP fuses the measurement data of the IMU array, predicts the actual acceleration and angular velocity of the object, and uses the positioning algorithm to verify it. In the localization experiment, the PDR localization accuracy using the prediction data fused by MLP is 33. 9% higher than the PDR localization accuracy using a single IMU measurement data; it is 20. 8% higher than the PDR localization accuracy using the simple average processing IMU array measurement data; it is 11. 6% higher than the PDR localization accuracy using the IMU array measurement data fused by the least square method, which proves the feasibility and effectiveness of the method proposed in this paper.