The high-precision measurement of the six dimensional acceleration sensor can effectively improve the control effect of the chassis anti-rollover control system, but the inter-dimensional coupling of parallel elastic elements can bring nonlinear errors to the sensor. The use of extreme learning machine algorithm for calibration and decoupling can effectively improve the measurement accuracy of the sensor. However, the traditional extreme learning machine nonlinear decoupling algorithm has low accuracy. The use of the sparrow search algorithm can obtain the optimal initial weights and thresholds of the extreme learning machine. At the same time, the maximum between-class variance method is integrated into the sparrow algorithm optimized extreme learning machine, which can explore the inherent coupling relationship of the six dimensional acceleration sensor. By converting the traditional black-box extreme learning machine model into a gray-box model, a decoupling algorithm for sparrow search optimization gray box extreme learning machine (SSAGB-ELM) is proposed. Through experimental verification, the nonlinear decoupling accuracy of the parallel six dimensional acceleration sensor using this algorithm is significantly improved, with a maximum error of 0. 023% for class I errors and 0. 046% for class II errors. The decoupling time is 1. 095 seconds, which can effectively solve the nonlinear coupling problem of six dimensional acceleration sensors.