Research on optimization of big data processing and analysis algorithm based on deep learning

This paper focuses on the optimization of big data processing and analysis algorithm based on deep learning, and puts forward a comprehensive optimization framework to solve the bottleneck problem of traditional methods in dealing with large-scale, high-dimensional and real-time data. The framework focuses on three aspects: model lightweight and acceleration, distributed training efficiency improvement and dynamic feature engineering. In the aspect of model lightweight, the parameters and memory occupation of the model are significantly reduced by dynamic structural pruning and mixed precision quantization technology, while maintaining high accuracy and reasoning speed. The distributed training optimization framework combines topology-aware gradient compression and heterogeneous resource intelligent scheduling strategy, which effectively reduces communication overhead and improves resource utilization, and is suitable for large-scale cluster and mixed hardware architecture training scenarios. The dynamic feature engineering module realizes efficient feature extraction and model generalization ability through multi-head spatio-temporal attention mechanism and sparse self-coding feature enhancement method. The experimental results show that the proposed optimization method has achieved remarkable results in model lightweight, distributed training communication optimization and feature engineering performance, which provides an efficient and flexible solution for the application of deep learning in big data processing and analysis.