Accurate measurement of the acceleration sensor output value is a necessary prerequisite for the prediction of relevant data. In order to compensate the output error of accelerometer sensor caused by manufacturing process and measurement environmental impact and accurately predict the output value of accelerometer sensor, an acceleration sensor error compensation and numerical prediction method based on adaptive singular spectrum and neural network is proposed. Firstly, the cause of the output error of the acceleration sensor is analyzed. Secondly, an adaptive singular spectral method is proposed for the acceleration sensor error compensation according to the singular entropy order determination denoising method. Finally, the radial basis function (RBF) neural network is selected as the numerical prediction method for the acceleration sensor output data, and the particle swarm optimization algorithm is used to optimize the initial parameters of the RBF neural network. The experimental results show that the adaptive singular spectral method can effectively compensate the output error of the acceleration sensor, and different adaptive parameters can be selected to meet different error requirements, and the RBF neural network optimized by the particle swarm optimization algorithm can effectively predict the output value of the acceleration sensor.