Aerospace information acquisition and image generation based on supervised contrastive learning

In order to improve the efficiency of obtaining open source aerospace information,and solve the problems of long open source aerospace information content,relatively limited quantity,poor robustness of commonly used text classification models, and unintuitive text information, this paper proposes a method for aerospace information text classification based on supervised contrastive learning. The method is based on the bidirectional long short- term memory(BiLSTM)network with the attention mechanism, integrates comparative learning technology,processes and analyzes open source information,efficiently screenes out aerospace information,and uses the unCLIP(un-Contrastive Language- Image Pre-Training) model to generate an image corresponding to the information. The experimental results show that compared with commonly used text classification methods such as CNN(Convolutional Neural Networks),BiLSTM, Transformer and BiLSTM-Attention,this method performes well in accuracy,recall and F1- Score,among them,F1-Score reaches 0. 97. At the same time,information is presented in the form of images to make information clearer and more intuitive. It can make full use of open data resources on the network,effectively extract open- source space information and generate corresponding images,which is of great value to the analysis and research of aerospace information.