Research on Big Data Processing Technology Based on Machine and Deep Learning and System Framework Construction

In this paper, we propose and build a deep learning-based big data analytics system, which aims to overcome the limitations of traditional analytics methods in handling large-scale and multi-type data. The system integrates advanced data processing techniques and deep learning models, aiming to achieve efficient data import, automated preprocessing, and accurate recognition and prediction of complex data patterns. The system utilizes the Spark architecture to achieve a flexible multi-type data import mechanism, supporting data from Hbase, NoSQL and various cloud storage services. It integrates a variety of data preprocessing methods, including data cleaning, and provides feature engineering methods, including cluster analysis and statistical analysis, for regular types of data. The system has a built-in RNN network model for processing and fitting time series data, as well as built-in algorithmic models such as SVM for conventional data types. Through real case applications, the system shows excellent performance in the field of financial risk prediction and natural speech recognition.