Statistical Feature Extraction and Classification of Complex Data Based on Deep Learning

This research is focused on elucidating the extraction of statistical features and the classification methodologies for intricate data leveraging deep learning techniques. The objective is to address the challenge of proficiently handling and precisely categorizing diverse forms of complex data in practical scenarios. Within this study, a composite model has been devised, merging the functionalities of Convolutional Neural Networks (CNN) with Recurrent Neural Networks (RNN), and augmenting the framework with an Attention Mechanism (AM) to bolster the capability of the model in extracting features and classifying intricate datasets. Through extensive experimentation, this paper validates the efficacy and versatility of the proposed model across diverse data types, including images, texts, and audio. The experimental findings demonstrate that, in contrast to traditional machine learning methods, the deep learning model proposed in this study excels at capturing abstract data features, thereby enhancing classification accuracy and generalization capability. The research presented herein offers novel insights and methodologies for the domain of statistical feature extraction and classification of complex data, thus playing a pivotal role in advancing the practical applications of deep learning technology.