Research on Electronic Information Feature Extraction and Classification Recognition Algorithm Based on Deep Learning

Abstract

In order to make up for the shortcomings of traditional electronic information characteristics and classification recognition algorithms and improve the application ability of the technology, a special extraction algorithm based on the performance of deep multilayer autoencoders is required, and the training of deep learning network model is not supervised with pretraining and the supervision of the margin-based Fisher guidelines. The regularization means in the process of data generating pretraining and carving will prevent the prefining training. Experimental results on multiple data distributions further determine the efficiency of the algorithm. By analyzing the depth learning model, the image information in electronic information is used as an example, and its extraction technology is discussed, and the direction is indicated further. The experimental results show that the acceptance performance of the DMFA algorithm is improved in some cases compared with similar algorithms. And in Ionosphere, PIMA, IRIS Data Set this algorithm acquires the improvement effect, but the results obtained in other data sets are not ideal, thereby obtaining the most use of electronic information feature extraction and classification recognition.