This study is interested in extracting representative features of the ultrasonic phased array image intelligent classification of the internal defects of naval gun mounts. Thus, an improved sparse self-encoding network model (RSAE) is proposed to realize the re-expression of sample data. First of all, in intelligent classification, the deterministic initial weight will lead to the best or worst result of neural network training, but in complex problems, it is very likely to get the worst result; at the same time, the neural network uses random weights. The results of training multiple times fluctuate greatly, which is not conducive to the performance evaluation of the network model. Therefore, this paper does not directly use the correlation parameter between the feature and the defect category as the initial feature weight of the RSAE. Instead, given a cell, the correlation parameter between the feature and the defect category is located in this cell. Then, on this basis, the optimization goal is to minimize the reconstruction error of training sample data, minimize the deviation of similar sample data, and maximize the difference of sample data between classes to realize the re-expression of sample data. The experimental results show that the advanced features obtained by the improved sparse autoencoder proposed in this paper are better than the original features in pattern recognition. This network can be used to more accurately identify the types of internal defects in the welds of naval gun mounts.

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