Author(s)

Fei Yin

Corresponding Author

Fei Yin

Abstract

This article focuses on the exploration of deep learning (DL) in the field of statistical modeling and application of unstructured data. In view of the complexity and importance of unstructured data processing, the research aims to build an efficient model with the help of DL technology to mine the value of such data. By preprocessing unstructured data such as images and texts, DL model frameworks such as convolutional neural network (CNN) and Long Short-Term Memory (LSTM) are designed and used respectively, and the model is trained by combining random gradient descent algorithm. In the application of medical image diagnosis, the CNN model based on lung X-ray images has a diagnostic accuracy of 85% for pneumonia and 88% for tuberculosis. In the analysis of social media public opinion, the LSTM-based model has an accuracy rate of 80% for positive emotion recognition and 83% for negative emotion recognition. The research results show that DL has achieved remarkable results in statistical modeling of unstructured data, and can effectively handle complex unstructured data. However, there are also some problems such as high data labeling cost and poor model interpretability, and related technologies need to be further optimized and improved in the future.

Keywords

Deep Learning; Unstructured Data; Statistical Modeling; Medical Imaging Diagnosis; Social Media Public Opinion Analysis