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Steel plate is widely used in aerospace, automobile, oil pipeline and other national economy industries, so it is necessary to find an appropriate method to test its mechanical properties, including yield strength, tensile strength and elongation, otherwise it will leave a safety hazard; At present, most detection methods of steel plate mechanical properties rely on destructive detection, and there is a certain relationship between steel plate microstructure and electromagnetic characteristic parameters. Therefore, a mechanical properties detection method based on Electromagnetic Acoustic Transducer(EMAT) and magnetostriction was proposed. The EMAT signals of steel plate were obtained by experiments. Feature extraction was carried out to analyze the correlation between characteristic parameters and mechanical properties. Stepwise regression and radial basis neural network functions were used respectively to establish the relationship between characteristic parameters and mechanical properties. Both models have high prediction accuracy, which represents the feasibility of the proposed method.