Designing an Efficient Framework for Large-Scale Data Processing and Analysis Based on Deep Learning Technology

This paper presents a comprehensive framework designed for the efficient processing and analysis of large-scale data through the integration of deep learning technologies. The framework leverages the capabilities of deep learning to enhance the accuracy and speed of data analysis, addressing the challenges posed by the sheer volume and complexity of modern datasets. It incorporates advanced deep learning models, including convolutional neural networks and sparse coding, to extract and analyze features from large datasets effectively. The framework is built upon a robust architecture that integrates with existing big data technologies like Hadoop, Spark, and TensorFlow, facilitating scalable and distributed data processing. Experimental results demonstrate significant improvements in performance analysis and prediction accuracy across various application domains, highlighting the framework's effectiveness in harnessing deep learning for large-scale data analysis. This study contributes to the field by proposing a scalable, efficient solution for data-driven decision-making and opens new avenues for research in deep learning applications for big data.