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To break the the limitations as slow analysis efficiency and poor synchronization, a dynamic performance evaluation model for high-speed permanent magnet motors based on multi-parameter evaluation is proposed. The hot card is used to fill in missing values to complete preprocessing. The grey relational analysis model (GRA) is designed to obtain the correlation between various attribute columns. The four-column attribute parameters of the motors after dimensionality reduction are obtained through the set of thoughts. A 4-5-1 three-layer neural network structure was established. By changing the number of expected attribute groups obtained by the greedy algorithm to 5 groups and increasing the parameter settings of the neural network, the optimized motor test data analysis model was designed. Within the range of allowable relative error that is 0.05, The accuracy of the prediction of operating efficiency has been increased from 90% to 94%. The experiments show that the optimized gray BP neural network model can be effectively used to predict the operating efficiency of motors, which is beneficial to the intelligent production of motor manufacturing and the application of machine learning in motor evaluation.