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The error sources of AACMM were many and complex, and the measurement space error was uncertain.In order to obtain the measurement space error of AACMM accurately, the standard cone was measured on AACMM, and also obtain the training data sample and test data sample, the AACMM’s measurement space error model was built up by BP neural network,and the particle swarm optimization algorithm (PSO) was introduced to optimize the convergence speed and the operation speed of BP neural network, and the prediction and verification of the model was carried out. Results show that BP neural network and PSO-BP neural network both can predict the measurement space error,the prediction results of PSO-BP neural network model are more accurate, and the relative error is smaller.