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Electromagnetic non-destructive testing technology is a research focus in the field of non-destructive testing. This paper proposes a RBF neural network based on FPFA parameter optimization for the processing of electromagnetic ultrasonic. First of all, the acquisition of electromagnetic ultrasonic signals is realized through FPGA programming, and the amplification circuit is designed to amplify the original electromagnetic ultrasonic waves. It has met the needs of RBF neural network; a K-means clustering algorithm is proposed to calculate the radial basis in RBF. The parameters of the center and width of the function optimize the RBF algorithm. The initial clustering center of the K-means clustering algorithm is difficult to determine, which will cause the parameters of the RBF algorithm to be unable to optimize. The KL divergence is proposed and the data density analysis method is used to calculate the K-means algorithm. Cluster center. Experiments show that the clustering results of the improved K-means algorithm are more accurate than the traditional K-means algorithm, and the parameter-optimized RBF neural network is more accurate than the traditional RBF neural network in predicting the defects of the engine turbine blades.