Research on Data Processing of Personnel Target Damage Caused by Blast Wave Based on CNN-LSTM Hybrid Model

Considering the complexity and nonlinear characteristics of explosion shock wave damage data to personnel targets, as well as the limitations of traditional data processing and analysis methods, this study analyzes the features of such data and constructs a hybrid model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). The model’s performance is validated through a numerical example and compared with traditional commonly used models. The results indicate that the CNN-LSTM hybrid model enhances accuracy and precision in processing explosion shock wave damage data to personnel targets. It demonstrates certain advantages in predicting and classifying the extent of damage caused by explosion shock waves, providing an effective approach for the processing and analysis of shock wave damage data.